Planning, navigation and simulation systems and methods for minimally invasive therapy

ABSTRACT

Disclosed herein are planning, navigation and simulation systems and methods for minimally invasive therapy in which the planning method and system uses patient specific pre-operative images. The planning system allows for multiple paths to be developed from the pre-operative images, and scores the paths depending on desired surgical outcome of the surgery and the navigation systems allow for minimally invasive port based surgical procedures, as well as craniotomies in the particular case of brain surgery.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.61/800,155, titled “PLANNING, NAVIGATION AND SIMULATION SYSTEMS ANDMETHODS FOR MINIMALLY INVASIVE THERAPY” and filed on Mar. 15, 2013, theentire contents of which is incorporated herein by reference.

This application also claims priority to U.S. Provisional ApplicationNo. 61/924,993, titled “PLANNING, NAVIGATION AND SIMULATION SYSTEMS ANDMETHODS FOR MINIMALLY INVASIVE THERAPY” and filed on Jan. 8, 2014, theentire contents of which is incorporated herein by reference.

This application also claims priority to U.S. Provisional ApplicationNo. 61/845,256, titled “SURGICAL TRAINING AND IMAGING BRAIN PHANTOM” andfiled on Jul. 11, 2013, the entire contents of which is incorporatedherein by reference.

This application also claims priority to U.S. Provisional ApplicationNo. 61/900,122, titled “SURGICAL TRAINING AND IMAGING BRAIN PHANTOM” andfiled on Nov. 5, 2013, the entire contents of which is incorporatedherein by reference.

FIELD

The present disclosure relates to planning, navigation and simulationsystems and methods for minimally invasive therapy.

BACKGROUND

In the field of medicine, imaging and image guidance tends to be asignificant component of clinical care. From diagnosis and monitoring ofdisease, to planning of the surgical approach, to guidance duringprocedures and follow-up after the procedure is complete, imaging andimage guidance provides effective and multifaceted treatment approaches,for a variety of procedures, including surgery and radiation therapy.

Targeted stem cell delivery, adaptive chemotherapy regimens, andradiation therapy are only a few examples of procedures utilizingimaging guidance in the medical field.

Advanced imaging modalities such as Magnetic Resonance Imaging (“MRI”)have led to improved rates and accuracy of detection, diagnosis andstaging in several fields of medicine including neurology, where imagingof diseases such as brain cancer, stroke, Intra-Cerebral Hemorrhage(“ICH”), and neurodegenerative diseases, such as Parkinson's andAlzheimer's, are performed. As an imaging modality, MRI tends to enablethree-dimensional visualization of tissue with high contrast in softtissue without the use of ionizing radiation. This modality is oftenused in conjunction with other modalities such as Ultrasound (“US”),Positron Emission Tomography (“PET”) and Computed X-ray Tomography(“CT”), by examining the same tissue using different physical principalsavailable with each modality.

CT is often used to visualize boney structures, and blood vessels whenused in conjunction with an intra-venous agent such as an iodinatedcontrast agent. MRI may also be performed using a similar contrastagent, such as an intra-venous gadolinium based contrast agent which haspharmaco-kinetic properties that enable visualization of tumors, andbreakdown of the blood brain barrier.

These multi-modality solutions may provide varying degrees of contrastbetween different tissue types, tissue function, and disease states.Imaging modalities can be used in isolation, or in combination to betterdifferentiate and diagnose disease.

In neurosurgery, for example, brain tumors are typically excised throughan open craniotomy approach guided by imaging. The data collected inthese solutions typically consists of CT scans with associated contrast,such as iodinated contrast, as well as MRI scans with associatedcontrast, such as gadolinium contrast. Also, optical imaging is oftenused in the form of a microscope to differentiate the boundaries of thetumor from healthy tissue, known as the peripheral zone. Tracking ofinstruments relative to the patient and the associated imaging data isalso often achieved by way of external hardware systems such asmechanical arms, or radiofrequency or optical tracking devices. As aset, these devices are commonly referred to as surgical navigationsystems.

Previously known systems for multi-modality imaging for planning andnavigation include integration of the imaging data of the surgery suitein an operating room. Technologies have allowed these three-dimensionalmodalities, including PET, CT, MRI, 3D US and two-dimensional modalitiessuch as X-ray and US, to be viewed together to create image sets, usedin the operating room. These image sets can be used to assist surgeonsin better resecting diseased tissue such as cancer, to guide repair ofvascular defects such as stroke and ICH, to deliver therapies forpsychiatric conditions such as major depression or obsessive compulsivedisorder, to perform procedures such as deep brain stimulation (“DBS”)for Parkinson's, Alzheimer's and Huntington's, and to guide radiationoncologists for radiation therapy for brain tumors.

These solutions have attempted to integrate different imaging modalitiesinto the surgical suite, by use of intra-operative imaging; for exampleby registering and tracking real-time US images; by use of “C” shapedarms for X-ray or CT imaging (“C-arms”); for instance, by use ofdedicated MRI systems for specific parts of the anatomy, such as thehead; as well as use of movable MRI systems. Generally, these systems donot take full advantage of the ability to achieve better imaging withthe improved access afforded by the surgical procedure itself, nor isthe information acquired integrated into the procedure in ways thataddress the fundamental challenges associated with the diseasemanagement.

There is therefore a need for a multi-modality imaging system and methodthat achieves surgical planning and navigation by analyzing input(s)retrieved through the improved tissue access resulting from the surgicalprocedures themselves.

Furthermore, there is a need for effective recording registration orintegrating images and other inputs in a meaningful way. Additionally,there is a need to integrate other valuable data points related tosurgical tools, or physics of the tissues themselves. There is thereforea need for a multi-modality imaging system and method that achievessurgical planning and navigation by meaningfully integrating a number ofdata points retrieved during, before and after surgery to provideimproved surgical and navigation systems. There is also a need for asystem and method that utilizes information specific to the surgicalprocedure and tools to provide improved navigation and planning.

Furthermore, imaging in current solutions is often performed on largesections of tissue, such as brain tissue, accessed by open surgicalapproaches that are highly invasive to the patient. There is also agrowing class of procedures, including neurosurgical procedures, whichideally would require only minimally invasive navigation and imagingsystem approaches. For example, ICH repair, stroke repair, deep braintumor surgery, intra-axial brain tumor surgery, endo-nasal surgery, suchas pituitary or brain-stem surgery, stem-cell therapy, directed drugdelivery, and deep brain stimulator delivery are all examples ofprocedures that are well suited to minimally invasive approaches. Manysurgical approaches in neurosurgery have become more dependent onminimally invasive approaches to resect diseased tissue, modify vascularand clotting issues, and maintain as much healthy neural tissue aspossible. Current intra-operative surgical systems such as navigationand imaging solutions, however, tend to be lacking. Although approachesto remove tissue through endo-nasal approaches, access port-basedapproaches, and positioning of electrical stimulation devices havebecome important procedures, medical imaging and navigation procedureshave not evolved to accommodate the specific needs of these approaches.

There is therefore a need for a multi-modality imaging system and methodthat achieves surgical planning and navigation through minimallyinvasive means and approaches.

Also, as port based procedures are relatively new, the detailedapplication of imaging to such a procedure has not been anticipated, norhas the interface between known devices' impact on tissue beenintegrated into a planning system. In craniotomies, the complexity ofthe multiple contrast mechanisms used in known systems can overwhelmsoftware system architectures. Furthermore, the complexities associatedwith tissue shift that occurs during surgery are not well addressed.There is therefore a need for a system and method for pre-operative andintra-operative planning and navigation to allow for minimally invasiveport based surgical procedures, as well as larger, open craniotomies.

In current systems, a radiologist, neurologist, surgeon or other medicalprofessional normally selects an imaging volume based on diagnosticimaging information, or clinical information related to the patient.This imaging volume is often associated with a suggested trajectory toapproach the surgery, for instance a needle insertion path. Onedisadvantage of current systems, however, is that this informationregarding tumor location and trajectory can typically not be modified orinteracted with in the surgical suite, resulting in limited utility ofthis detailed information if additional information during the surgerycomes to light, for instance the location of a vessel or criticalstructure in conflict with the pre-selected trajectory. There istherefore a need for a system that provides real-time surgical procedureplanning correction.

SUMMARY

The present invention is directed to a planning system for minimallyinvasive therapy. In the present invention systems and methods areprovided for planning a pathway to a target location in tissue within apatient's body. The system consists of a storage medium to storepre-operative imaging volumes, a surgical outcome criteria associatedwith anatomical portions of the body, and a processor, in communicationwith the storage medium and outcome criteria, to identify, score andsave one or more surgical trajectory paths.

In one embodiment, the system comprises a storage device, a computerprocessor that works cooperatively to receive, store and compute inputsand surgical trajectory paths, and displays the results on a userinterface.

In a further embodiment, a computer implemented method for planning apathway location to a tissue within a patient's body is disclosed. Themethod comprises the steps of receiving inputs through the userinterface of a computer, producing a 3D image containing entry points tothe tissue, computing and storing one or more surgical trajectory pathsbased on a surgical outcome criteria, and displaying a selectedtrajectory path at the user interface.

A system for planning brain surgery is further disclosed. The systemcomprises a storage device to store at least one pre-operative 3Dimaging volume, and a computer processor that receives inputs (i.e.,sulci entry points, target locations, surgical outcome criteria, 3Dimaging volume), computes a score based on a surgical outcome criteriaand displays one or more trajectory paths based on the score.

In a further embodiment, a system for planning a pathway location to atissue within a patient's body is disclosed. The system comprises astorage medium, a display, a user interface and a computer program withmultiple code segments configured to produce a 3D image, receive userinputs, compute one or more point-wise trajectory paths related to asurgical outcome criteria, and assigning a relevant score to the one ormore trajectory paths.

In a further embodiment, a system for planning a pathway location to atissue within a patient's body is disclosed. The system comprises astorage medium, a display, a user interface and a computer program withmultiple code segments configured to produce a 3D static or animatedimage, receive user inputs, store a pre-operative imaging volume,compute one or more point-wise trajectory paths relative to known pointsin the imaging volume that relate to a surgical outcome criteria,assigning a score to the one or more trajectory paths, and exporting theone or more such paths.

A further understanding of the functional and advantageous aspects ofthe invention can be realized by reference to the following detaileddescription and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments disclosed herein will be more fully understood from thefollowing detailed description thereof taken in connection with theaccompanying drawings, which form a part of this application, and inwhich:

FIG. 1 is a block diagram showing system components and inputs forplanning and scoring surgical paths as disclosed herein.

FIG. 2 is a block diagram showing system components and inputs fornavigation along the surgical paths produced by the planning system ofFIG. 1.

FIG. 3 is a block diagram showing system components and inputs forpost-operative data analysis.

FIGS. 4A and 4B shows an embodiment of the present method and system,wherein processor(s) have identified fiber tract bundles to aid inoptimal selection of surgical approach.

FIG. 5 is a flow chart illustrating the processing steps involved in theplanning system and method disclosed herein.

FIG. 6 shows an exemplary, non-limiting implementation of computercontrol system for implementing the planning and guidance method andsystem disclosed herein.

FIG. 7 shows an output of an embodiment of the present method and systemshowing visualization patient anatomy using three orthogonalprojections. The two display panes in the top row and left most pane inthe bottom row illustrate 2D projections that are orthogonal to eachother.

FIG. 8 shows an illustration of highlighting of tracts against 2Dpatient data expected to be intersected by a surgical tool for a shownpose or orientation.

FIG. 9 shows an illustration of the same patient data as shown in FIG.8, however with different tracts intersected by the surgical tool for adifferent pose relative to a target in the brain.

FIG. 10 shows a visualization of craniotomy extent using a selectedtrajectory and surgical tool, and showing the space available formanipulating the surgical tool during surgery.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosure will be described withreference to details discussed below. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosure.

As used herein, the terms “comprises” and “comprising” are to beconstrued as being inclusive and open ended, and not exclusive.Specifically, when used in the specification and claims, the terms“comprises” and “comprising” and variations thereof mean the specifiedfeatures, steps or components are included. These terms are not to beinterpreted to exclude the presence of other features, steps orcomponents.

As used herein, the term “exemplary” means “serving as an example,instance, or illustration,” and should not be construed as preferred oradvantageous over other configurations disclosed herein.

As used herein, the terms “about” and “approximately” are meant to covervariations that may exist in the upper and lower limits of the ranges ofvalues, such as variations in properties, parameters, and dimensions.

As used herein, the term “patient” is not limited to human patients andmay mean any organism to be treated using the planning and navigationsystem disclosed herein.

As used herein the phrase “surgical tool” or “surgical instrument”refers to any item that may be directed to a site along a path in thepatient's body. Examples of surgical tools may include (but are notnecessarily limited to) scalpels, resecting devices, imaging probes,sampling probes, catheters, or any other device that may access a targetlocation within the patient's body (or aid another surgical tool inaccessing a location within a patient's body), whether diagnostic ortherapeutic in nature.

As used herein, the phrase “optical coherence tomography” or “OCT”refers to an optical signal acquisition and processing method whichcaptures micrometer—resolution, three-dimensional images from within anoptical scattering media such as biological tissue. OCT is aninterferometric technique, which normally uses near-infrared light. Theuse of the relatively long wavelength allows it to penetrate into thescattering medium. An advantage of OCT in the context of medical imagingis that it provides tissue morphology images that have a much higherresolution (better than 10 μm) which is currently better than otherimaging modalities such as MRI or ultrasound. However, currently OCT islimited to imaging 1 to 2 millimeters below the surface in typicalbiological tissue, whereas at deeper depths the proportion of light thatescapes without scattering is too small to be detected. The images canbe obtained ‘non-contact’ or through a transparent window or membranebut must be in line-of-sight with the target tissue.

As used herein, the phrase “polarization sensitive optical coherencetomography (PS-OCT)” refers to an imaging technique which provides depthresolved measurements of the polarization state of light reflected fromturbid media such as tissue. The measurement of the depth resolvedStokes parameters allows determination of the degree of polarization andoptical axis orientation in turbid media that can be modeled as a linearretarder.

As used herein, the word “ultrasound” or “US” refers an imagingtechnique using sound waves in the frequency range of about two toeighteen megahertz. The selected frequency for a particular medicalimaging procedure is often a trade-off between spatial resolution of theimage, and imaging penetration depth. Lower frequencies produce lowerresolution but can image deeper into the body, while higher frequencysound waves produce a higher resolution (due to smaller wavelength, andthus are capable of reflecting or scattering from smaller structures).The higher frequency waves also have a larger attenuation coefficient,and thus are more readily absorbed in tissue, limiting the depth ofpenetration of the sound wave into the body.

As used herein, the phrase “positron emission tomography” or “PET”refers to a nuclear medical imaging technique directed to generating athree-dimensional image of functional processes in the body. The PETsystem operates on the principle of detecting pairs of gamma rays whichare emitted by a positron-emitting radionuclide or tracer, which isinjected into the body. Three-dimensional images of the tracerconcentration within the body are then constructed by computer analysis.

As used herein, the phrase “computed tomography” or “CT”, also referredto as “X-ray computed tomography” or “x-ray CT” refers to a technologythat uses computer-processed x-rays to produce tomographic images(virtual ‘slices’) of specific areas of the scanned object.Three-dimensional images of the inside of the object being studied maybe generated using the technique of digital geometry processing from aseries of two-dimensional radiographic images taken around a single axisof rotation. CT scanning of the head/brain is typically used to detecthaemorrhaging, bone damage, tumors, infarction and calcifications, tomention a few. Of these, hypodense (dark) structures typically indicateedema and infarction, while hyperdense (bright) structures typicallyindicate calcifications and haemorrhaging. Tumors are often detectableby the swelling and anatomical distortion they cause, or by anysurrounding edema.

As used herein, the phrase “magnetic resonance imaging” or “MRI” refersto a medical imaging technique used in radiology to visualize internalstructures of the body and is used to study both anatomy and function inhealth and disease. MRI is the investigative tool of choice forneurological cancers as it is more sensitive than CT for small tumors.In addition, the contrast provided by MRI between the grey and whitematter of the brain make it the leading choice for many conditions ofthe central nervous system including, but not limited to demyelinatingdiseases. Furthermore, specialized MRI pulse sequences can be used togive different types of information. For example, “diffusion MRI” is anMRI sequence that measures water molecule diffusion in biologicaltissues and is clinically useful for the diagnoses of conditions, suchas stroke, or neurological disorders, such as multiple sclerosis, and isparticularly useful for understanding and visualizing the directionalityand connectivity of white matter tracts in the brain. Examples ofdiffusion MRI are diffusion tensor imaging (“DTI”) and diffusionweighted imaging (“DWI”). Also, “functional MRI” or “fMRI”, is anotherspecialized MRI sequence that is sensitive to changes in bloodoxygenation levels, and can be used to infer areas of increased corticalactivity. Typically with fMRI the patient is asked to perform aspecified task (e.g. motor activity, cognitive exercise), and thehighlighted areas in the fMRI scan can indicate which areas of the brainhad increased blood flow (and thus were more active) when such task wasbeing performed.

MRI may also be performed as a perfusion scan, which incorporates theuse of a contrast agent (typically Gadolinium) and observes how suchagent moves through tissue over time. The typical perfusion scantechnique begins with taking a baseline 3 d volume, injecting thecontrast agent, and then taking repeated scans thereafter (with thepatient remaining in the same scanning position during the scansession).

In the above three example MRI techniques (diffusion MRI, fMRI,perfusion MRI), what is generated is a 4d dataset (i.e. 3 d volumesevolving over time) which includes data relating to either waterdiffusion (diffusion MRI), blood oxygenation (fMRI), or a contrast agentmoving through tissue (perfusion MRI), in addition to the static imagingdata.

In some embodiments the systems and methods can include use oftractography. In the system and methods described herein, thedifferentiation between tumor and healthy tissue may be performed withDWI sensor(s) and associated processor(s) which use the diffusion ofwater through tissue of the brain, by Brownian motion, as the primarytissue contrast mechanism. The data acquired from the diffusion contrastscan can be acquired in a predefined gradient direction to enablevisualization of diffusion along a specific direction in the brain. Thisdirectional information can be used to generate connectivity mapsdefined by sets of vectors to generate fiber tracts in the brain;wherein these tracts correspond to water diffusing on the outside of thewhite matter tracts through the brain and correspond to the major nervefibers in the brain.

The different imaging modalities mentioned above can be combined to givegreater insight and more information that can be obtained using only onemodality alone. For example, PET scans can be taken in conjunction withCT and/or MRI scans with the combination images (called “co-registered”images) giving better information, and which may include both anatomicand metabolic information. For example, since PET imaging is most usefulin combination with anatomical imaging, such as CT, modern PET scannersoften include integrated high-end multi-detector-row CT scanners(so-called “PET/CT”). In these machines, the two types of scans can beperformed in a side-by-side sequence during the same session, with thepatient not changing position between the two types of scans, such thatthe two sets of images are more-precisely co-registered, so that areasof abnormality observed with the PET imaging modality can be moreaccurately correlated with anatomy observed from the CT images. This isvery useful in showing detailed views of moving organs or structureswith higher anatomical variation, which is more common outside thebrain.

Thus, as used herein, the phrase “registration” or “co-registration”refers to the process of transforming different sets of data into onecoordinate system, and “image registration” refers to the process oftransforming different sets of imaging data into one coordinate system.Data may be multiple photographs, data from different sensors, times,depths, or viewpoints. The process of “co-registration” in the presentapplication in relation to medical imaging in which images fromdifferent imaging modalities are co-registered. Co-registration isnecessary in order to be able to compare or integrate the data obtainedfrom these different modalities. Those skilled in the art willappreciate that there are numerous image co-registration techniquesavailable and one or more of them may be used in the presentapplication. Non-limiting examples include intensity-based methods whichcompare intensity patterns in images via correlation metrics, whilefeature-based methods find correspondence between image features such aspoints, lines, and contours. Image registration algorithms may also beclassified according to the transformation models they use to relate thetarget image space to the reference image space. Another classificationcan be made between single-modality and multi-modality methods.Single-modality methods typically register images in the same modalityacquired by the same scanner/sensor type, while multi-modalityregistration methods are used to register images acquired by differentscanner/sensor types. In the present disclosure, multi-modalityregistration methods are used in medical imaging of the head/brain, asimages of a subject are frequently obtained from different scanners.Examples include co-registration of brain CT/MRI images or PET/CT imagesfor tumor localization, registration of contrast-enhanced CT imagesagainst non-contrast-enhanced CT images, and registration of ultrasoundand CT, to name a few.

It will be appreciated that the planning and navigation methods andsystems disclosed herein are applicable to imaging modalities notnecessarily currently available. For example, with reference to MRI, newsequences, methods or techniques in addition to those outlined hereinmay further useful biomedical imaging information, which may be readilyincorporated into the methods and systems disclosed herein through anappropriate co-registration technique.

As used herein, the phrase “pre-operative imaging modality” refers tothe herein modalities and any other imaging techniques which have thenecessary tissue penetration to image anatomy prior to invasiveprocedures being initiated.

As used herein, the phrase “surgical outcome criteria” means theclinical goal and expected outcome of a surgical procedure as envisionedby a surgeon who is trained in such surgical procedures. In general, thesurgical intent of a brain tumor resection surgery is to remove as muchof the tumor as possible while minimizing trauma to the rest of thebrain and surrounding tissue structures (surrounding tissue structuresin this case including any tissue structure that is directly orindirectly affected during the surgical procedure). Examples ofsurrounding tissue structures to the brain include, but are not limitedto, dura, cerebrospinal fluid, and skull.

As used herein, the phrase “point-wise surgical trajectory path” meansany continuous (i.e. without breaks) line representative of a path whichpasses through a beginning point (also known as entry point), aconsecutive number of waypoints, and an end point representative of thetarget, wherein each point is connected to its adjacent points through acurved or straight line defined in 3D space; the path being arepresentation of the surgical trajectory used to fulfill one or moresurgical outcome criteria.

As used herein, the phrase “waypoint” means a point created between thebeginning and end points of a point-wise surgical trajectory path withwhich the path is required to traverse through in a sequence determinedby the surgeon to satisfy a surgical intent. In many cases, waypointsare points created to guide the point-wise surgical trajectory pathalong a desired trajectory. However, waypoints may also indicate pointson the trajectory where specific surgical actions may be undertaken. Forexample, a waypoint may be introduced along a trajectory used in brainsurgery to remind the surgical team that a biopsy samples may have to betaken. Alternatively, a waypoint may be used to send a message to anavigation system that parameters may have to be changed. For example,it may be desirable to have an external video scope (automatically orupon user confirmation) switch from a wide field of view duringcraniotomy, to a narrow field of view during opening of the dura.

As used herein, the phrase “3D image” means a display of an imagecontaining more than two dimensions of spatial information. This displayincludes but is not limited to, stereoscopic displays, dynamic computermodels with an interface allowing for rotation and depth selection,perspective views, and holographic displays. Additionally, it is wellknown in the field that a 3D image can be represented by a concatenationof 2D images of varying depths or angles therefore reference to a “3Dimage” is analogous to the reference to a set of distinct 2D images ofthe same object. It is also possible to create a 3D image directly from3D measurements in some modalities (e.g. MRI), so the concatenation of2D images is not normative. Furthermore, the term “volume” and “image”are used interchangeably in this context.

As used herein, the phrase “code segment” means a unit of codeexecutable on a computer such as an algorithm or a program. Embodimentsof the present disclosure may include multiple code segments. Codesegments are labeled by ordinal numbers, i.e. “first code segment,”“second code segment.” It should be understood that the ordinal numbersdo not signify a specific order with which the code must be executed orimplemented nor do they imply interdependence of the programs oralgorithms.

While the present method and system may be used for performing surgeryon any part of the patient's anatomy, it is particularly useful forperforming brain operation procedures as it makes advantageous use ofimaging information showing the inferred location and directionality ofnerve fascicles and major nerve fiber bundles in the brain. Embodimentsdescribed herein are configured to provide a system and method to detectand suggest a surgical corridor to an area of the brain for a givenprocedure, and predict the potential impact that approach would have onthe healthy brain tissue. Such an impact assessment would allow asurgeon to make a decision on approach using quantitative assessment.

For example, there are currently no clinically acceptable means ofperforming a biomechanical model of brain movement for minimallyinvasive corridor surgery. Current systems are generally not able todetermine the potential movement of brain tissue intra-operatively, tosuggest modified approaches to lesions; to suggest modified surgicalapproaches that would allow more diseased tissue to be resected, whileleaving more healthy tissue unaffected; and to evaluate the impact ofbrain and tissue shift as a result of tissue resection prior to actualresection (as most current scanning and surgical phantoms are containedin a solid container and hence shift in the matrix material is generallyminimal). Additionally, there is no means of performing the imagingregistration that is required to update pre-surgical plans, withmultiple imaging contrast datasets that that provide the appropriatebiomechanical information. In other embodiments, a means to manage grossbrain displacement, for instance, by way of a small craniotomy accesshole and using the natural orifices of the brain allows for simulationapproaches to be utilized in manners that can inform surgical approachesin ways not possible with current solutions.

Furthermore, there are no existing planning and training systems thatcan be used to plan and navigate through brain sulci. There istherefore, a need for surgical planning and training system and methodfor planning a trajectory along a corridor such as along the sulci, ascurrent moulds for surgical phantoms tend not to emulate the ridgestructure present on the surface of the brain.

Also, current training systems generally possess inherent inabilities tofine-tune the training session to the specific surgical scenario, astraining is known to be done using an agar gel that is encased in asquare mould with a grape located at the centre of the cube near thebottom, which fails to provide the surgeon with a clear understanding ofconstraints such as inhomogeneity, and orientation and tissuedisplacement under gravity and pressure. Other embodiments provide foran intelligent system and method which allows for practice of an entiresurgical procedure on a simulated platform, which may be useful forperforming a mock surgical procedure at least a day in advance of asurgical procedure to identify a patient's head orientation foridentification and placement of appropriate surgical tools in advance ofsurgery. This can be realized through the use of a surgical phantom thatclosely mimics the brain dimensions of the particular patient andlocation of tumor model at a geometrically accurate location in the saidbrain phantom.

The ability to image nerves, and establish surgical procedures to guidedevices, or resect tissue while sparing these nerves, requires theintegration of navigation technology, software planning systems,pre-operative imaging, and surgical tools. Embodiments of the describedsystem and methods to provide an interface from which a surgeon may plana minimally invasive approach based on the most up-to-date imaging thatis provided for that patient. As nerve bundles, in the context of whitematter tracts in the brain, represent a complex data set that may bebest represented in a three dimensional context, accuraterepresentations of this information relative to surgical approachprovided by the current systems and methods may be critical in order toprovide the best possible route to the target of interest. Representingsurgical tools and surgical approaches relative to these white mattertracts, and the target of interest (often a complex tumor geometry), hasnot been addressed in a manner to allow for effective trajectoryplanning for surgical approaches. In addition, the path for access, isoften selected to minimize the amount of grey and white matter that istraversed, without a careful consideration of what the white matter isattached to, (cortical banks of grey matter), or the condition of thewhite or grey matter, i.e. does it have the chance to recover or is theregion eloquent. In addition, the use of natural access corridors in thebrain has not been considered in the context of planning.

For instance, the natural folds of the brain, i.e. sulci, offer idealminimally invasive access pathways to deep locations in the brain. Inorder to utilize these corridors effectively, a novel software planningsystem and method are provided to process and calculate input data,represent it to a user, and provide quantifiable metrics to facilitatedecision-making.

The systems and methods described herein are useful in the fieldneurosurgery, including oncological care, neurodegenerative disease,stroke, brain trauma and orthopedic surgery; however persons of skillwill appreciate the ability to extend these concepts to other conditionsor fields of medicine.

Various apparatuses or processes will be described below to provideexamples of embodiments of the planning and navigation method and systemdisclosed herein. No embodiment described below limits any claimedembodiment and any claimed embodiments may cover processes orapparatuses that differ from those described below. The claimedembodiments are not limited to apparatuses or processes having all ofthe features of any one apparatus or process described below or tofeatures common to multiple or all of the apparatuses or processesdescribed below. It is possible that an apparatus or process describedbelow is not an embodiment of any claimed invention.

Furthermore, numerous specific details are set forth in order to providea thorough understanding of the embodiments described herein. However,it will be understood by those of ordinary skill in the art that theembodiments described herein may be practiced without these specificdetails. In other instances, well-known methods, procedures andcomponents have not been described in detail so as not to obscure theembodiments described herein.

Also, the description is not to be considered as limiting the scope ofthe embodiments described herein. Furthermore, in the followingpassages, different aspects of the embodiments are defined in moredetail.

Presented in this disclosure is a software and hardware system toprovide diagnostic, surgical planning, surgical guidance and follow-upimaging information to support surgical and image guided therapyprocedures. In an embodiment, an exemplary system consists of a computerprocessing unit, software algorithms, a display unit, input/outputdevices, imaging modalities, device tracking devices to facilitatemedical imaging information representation to facilitate surgicalprocedures. This system focuses on minimally invasive surgicalapproaches to managing neurosurgical disease as well as head and neckcancer; however, it is not limited to these applications. These conceptscan be used to address diseases throughout the body whereminimally-invasive approaches may be coordinated with pre-operativeimaging, and/or intra-operative imaging. The system is described in thecontext of neuro-surgical applications; however the general concept canbe extended to various applications described further on in thisdocument.

This disclosure describes methods and systems for pre-operative,intra-operative and post-operative planning and navigation to allow forminimally invasive surgical procedures. The systems and methods may beused as surgical planning systems and methods, or as combined planningand intra-operative guidance and navigation systems and methods, whereininformation collected during the surgical procedure is used to guide thenext surgical steps, or measure predicted patient outcome.

In an embodiment of the present methods and systems, there is providedone or more sensor(s) which detect input(s), such as pre-operative datainput(s) and intra-operative data input(s); the sensor(s) being incommunication with one or more processor(s) that receive, record and/orprocess the input(s) detected by the sensor(s) to generate output(s)that may be useful for surgical planning, navigation and analysis.

FIG. 1 shows an embodiment of the present method and system, for use asa multi-modal surgical planning tool. The system and method can be usedas a surgical planning tool in the pre-operative stage. Persons of skillwill appreciate that the surgical planning steps depicted in FIG. 1, mayalso be repeated intra-operatively to further refine the surgicalapproach, such that the terms surgical planning and intra-operativenavigation may be used interchangeably.

In some embodiments, the systems and methods may include data inputsincluding but not limited to MRI (6), US, CT, other optical imagingsystems, and the models of surgical tools (1) and sensors. Imaging datamay be acquired by comparing various images of the patient's tissue andorgans, including co-registered data between DWI (diffusion weightedimaging) (4), DTI (diffusion tensor imaging) (3), and other imagingcontrast sequences and modalities. In an embodiment where the presentinvention is used in an intra-operative setting, to set or update asurgical path, data inputs may include examples from the above imaging,acquired through sensors, as is further disclosed herein. Sensor(s) mayinclude means for accurately and robustly tracking surgical tools,including optical or electromagnetic intra-operative trackingcomponents, and other means of registration (15) of the intra-operativedata sets to the pre-operative dataset. Registration methods caninclude, for example, any or a combination of the following: imageintensity matching based on similarity metrics such as sum of squaredintensity differences and mutual information, computed overneighborhoods or regions; image feature based registration such as edgematching; fiducial or anatomical feature based matching of common pointsdefined in multiple image modalities or coordinate spaces (such as atracking system's coordinate space and an MR image's coordinate space);surface matching techniques such as surface mesh matching.

Surfaces can be manually outlined or automatically segmented from imagedata. Similarly, surfaces can be determined from the physical patient byoutlining with a tracked pointer tool or through a surface scanningtechnique (such as a laser rangefinder, structured light system orstereo camera). All matching and registration methods can be performedon a sub-region of an image or patient volume (such as what isvisualized through the port), to focus on a specific region of interest.Registration can be performed on multiple sub-regions jointly orindependently and an interpolated registration can be inferred betweenthese independent regions. Once the images are registered they form aninput to a data analysis module (16).

Persons of skill will appreciate that the sensor(s) can also includeplanning, navigation and modeling components, contextual interfaces,intra-operative imaging devices, devices for bi-polar suction, tissueablation and tissue cutting with attached imaging, trackingtechnologies, including external and internal tool tracking (lightdeflection, capacitive, strain gauge), automated guidance externalimaging systems, semi-automated external positioning arms with turrets,internal semi-automated manipulators, multiple beam delivery systems,databases with adaptive learning networks, imaging and spatially linkedpathology systems, imaging devices which respond to the context they areused in, as well as user interfaces which respond to the context andenvironment they are used in.

Inputs and sensor(s) can also include keyboards, touch screens, pointersor tools that act as pointing devices, mice or gesture controlcomponents.

The pre-operative data input(s) of the exemplary systems and methodsdescribed herein can include pre-operative image data; biomechanicalmodels of tissues and organs; and mechanical models of surgical tools.The intra-operative data input(s) can include images from variousmodalities including MRI, CT or PET, as well as data from tracking ornavigation systems, including tracked surgical devices, such asscissors, ablation devices, suction cutters, bi-polars, tracked accessport devices and automated guidance external imaging systems. In someembodiments, particular surgical procedures 14 and clinical criteria 13,selected for example on a patient by patient basis, can be utilized asadditional input(s) to assess optimal surgical plans.

In some embodiments, the processor(s) may include planning module(s) 12that analyze input(s) from 13 and 16 to define surgical approaches.These may include open craniotomies, DBS stimulator locations, biopsysites, port-based or minimal corridor approaches and endo-nasal basedapproaches based on a variety of input(s) and rule-based calculations.In further embodiments, the processor(s) may include navigationmodule(s) that analyze input(s) to provide visualization and otheroutputs during procedures, such as tool tracking, and contextualinformation.

In other embodiments, the processor(s) may segment tissue structuressuch as tumors, nerves and nerve tracts, brain structures, such asventricles, sulci, cortex, white matter, major white matter bundles,vasculature such as arteries and veins, and boney structures such asskull and brain stem, for planning and navigation purposes.

Output(s) can include 2D and 3D composite images, used for guidance,including tissue extraction guidance and guidance for devices includingDBS probes and biopsy probe. Persons of skill will appreciate thatoutput device(s), including monitors or laser pointers can also beincluded in the systems and methods described herein to provide userswith feedback on the processes of the system.

Visualization output(s) can include contextual volume imaging; pointsource imaging which involves imaging only the regions of interest thatare important at that point of the surgical procedure; imaging to checkpositioning before instrument insertion or removal, imaging to updatetissue maps after resection, as well as imaging to resect maximal tumorwhile limiting damage to healthy or recoverable tissue. In addition, theuse of common contrast mechanisms between imaging modalities used in thesystems and methods described herein may allow the processor(s) togenerate accurate registration between modalities, and meaningfulvolumetric imaging updates during procedures.

Output(s) can also include path planning or correction data for asurgical approach by way of feature detection, positions for proceduressuch as craniotomies, locations for pinning and immobilization ofpatients. Output(s) can also include data on selection of surgicalapproach, for instance trans-sulcal approaches to avoid vessels andfiber bundles. For example, the output(s) can also include sulci basedapproach paths to minimize white matter and grey matter insertiondamage. Further output(s) can included parametric curves or volumes todefine or facilitate a time evolution of data such as the chosen paths,tissue deformations, time animation of data sets with time components(e.g. Doppler US or fMRI), or arbitrary combinations of such data.

General Planning Method for any Part of a Patient's Body

Disclosed herein is a planning method executed on a computer forplanning a surgical trajectory pathway from a surface location on apatient's body to a target location within the body to be approached andoperated on. The planning method is quite general and can apply to anypart of a patient's body. The method includes acquiring pre-operativeimages of a portion of the patient's body to be operated on using atleast one imaging modality configured for acquiring a 3D image data setor volume and storing the 3D image data set or volume in a storagemedium. It will be understood that more than one imaging modality may beused, in particular where the anatomical part of the patient to beoperated on would best be suited to a certain type or combination ofimaging modality. An image of a 3D volume is produced from the 3D imagedata set which contains potential entry points into the body along withone or more targets to be approached. The image of the 3D volume isstored the storage medium. Once the location of the one or more targetshas been identified, their location(s) may be adjusted and/or confirmedon 2D planar estimates or projections of the data, referred to as“reformats”. This technique visualizes representations of one or more 2Dplanes through the 3D space containing the image data. Such planes areoften orthogonal, and often shown in canonical (axial, coronal,sagittal) directions as a “multiplanar reconstruction” or “MPR”. Othervariants exists, such as “radial stacking” where one or more planes areshown through a common axis about which they all rotate. However it willbe appreciated that any configuration of planes, containing image datafrom a single source or fusions of multiple sources may be used. Where3D data exists (such as from an MRI, CT, or 3D ultrasounds volume)reformatted images may be produced by interpolating from the samplinglattice with any appropriate standard interpolation scheme. If thedesired data is two dimensional in nature (such as an Xray, or 2Dultrasound) the data may be projected onto the reformat plane, or itsplanar intersection only presented, or both approaches fused as desired.Once the reformatted planes are presented to the user, they may adjustthe each planar location within the 3D space, and refine the targetingposition relative to each planar representation until they are satisfiedthat they have identified the correct location in 3D space. Using theimage of the 3D volume, the method includes designating a location of atleast one entry point into the patient's body for a surgical apparatusand specifying, from the one or more target locations, a specific targetlocation to be approached. Designation of the location of the one ormore potential entry points and target location(s) may be done in one ofseveral ways. For example, the clinician may select the entry point(s)by overlaying a mouse cursor on point(s) of the 3D rendered brainsurface and clicking. Alternatively the system may be programmed toautomatically select or suggest potential entry point(s), based on acertain criteria (such as the use of sulcal paths for entry). Forexample, given an image volume (e.g. a T1 MRI image), a segmentation ofthat image including labeling of portions of the image (into whitematter, grey matter, dura, and sulci), and a target, the system could beused to limit or suggest against certain entry locations. The systemcould generate the best sulcal entry points based on, for example,minimizing the number of impacted fibres, distance from the sulcalboundary to the target, and volume of white and/or grey matter displacedby the approach path. Such points could be found by exhaustive search orvarious standard methodologies (e.g. energy minimization). A simpleapproach could be advanced by utilizing additional information (moresegmentation labels, biomechanical modelling, fluid dynamic modeling) toapply a more sophisticated analysis to the generation of the “best”candidate points. The surgeon would select from amongst these “best”candidate points, or could reject them and select one manually.

One or more surgical intents or surgical outcome criteria to besatisfied by a surgical trajectory path from the entry point to thespecified target location is then selected, and, based on the surgicalintent, optionally one or more waypoints between the designated locationof the entry point and the specified target location which areconsistent with the surgical intent may be selected.

In another embodiment, surgical paths may be traced and recorded throughuse of a navigation system, while a clinician using tracked toolsattempts different approaches toward a target in a brain phantom thathas been fabricated to model the actual patient's anatomy.

One or more point-wise surgical trajectory paths from the designatedentry point to the specified target location are then computed with theone or more point-wise surgical trajectory paths passing through one ormore waypoints between the entry point and the specified target locationto define a surgical trajectory path from the designated entry point tothe selected target location. These trajectories could be specifiedmanually by a clinician, or they can be computed automatically. Anexample automatic computation could include the following. Given an MRIT1 image and a surgical entry point and a target specified within it,the system specifies a lattice (e.g. the image voxel centers, or anyother lattice chosen for convenience). The lattice infers a graph ofconnections between all neighboring voxels for a chosen connectionscheme (i.e. which may allow only 6-way neighbours without diagonalconnections, or 27-way full connections, or any other subset). Eachconnection is given a weight (i.e cost) base on the pixel intensitiesintegrated along the direct path between lattice points. Now we apply astandard path finding algorithm (e.g. an A* search algorithm, forexample Hart, P. E.; Nilsson, N. J.; Raphael, B. (1968). “A Formal Basisfor the Heuristic Determination of Minimum Cost Paths”. IEEETransactions on Systems Science and Cybernetics SSC4 4 (2): 100-107) todetermine the best path. Variants of this approach can include moreterms in the cost function based on labeled regions of the brain,biophysical modelling, fluid dynamics, etc. if they are available.Variants may also include post processing of the path (e.g. smoothing)as desired. Waypoints may also be added by the clinician after automaticcomputation of the surgical trajectory path.

Once the one or more point-wise surgical trajectory paths have beenproduced they may be stored in the storage medium and visually displayedto the clinician. The one or more surgical intents may be selected bythe surgeon checking off from a list of surgical outcome criteriadisplayed on the computer display screen by overlaying the mouse overthe one or more listed intents and clicking them off. Furtherembodiments may include the use of a touch screen or a stylus, as wellas a monitor in connection with a video tracking system or other meansof delivering gestural input, or voice input. These surgical outcomecriteria will be different for different parts of anatomy being workedon, for example the list of criteria may be different for case of brainsurgery compared to spinal surgery.

The step of selecting a surgical intent or surgical outcome criteria tobe satisfied by a surgical trajectory path may include selecting one ormore anatomical features to be avoided (or to have minimal damage doneto them), or alternatively, selecting one or more regions to be passedthrough by the surgical path, and again this may be done by the surgeonplacing the cursor over the particular locations to be avoided or passedthrough, and clicking the cursor to store such particular location. Oncethe selection has been made, the locations of the one or more anatomicalfeatures is identified from the 3D volume image and one or more surgicalpaths may be calculated that avoid, or pass through, the one or moreanatomical features, as desired. Typical non-limiting examples ofanatomical features to be avoided, or to have minimal damage done tothem, include any one or combination of nerve damage, muscle damage,ligament damage, tendon damage, blood vessel damage, white matter braintract damage (in the case of brain surgery).

Identification of such structures can be provided to the system bydefining regions of interest, label maps, or other metadata relative toan imaging volume. Alternatively, the system can estimate suchstructures automatically and use them in other analysis. One method todo this would be by way of co-registering a detailed brain atlas withimage volume(s) being used, and then using the atlas labels used asinput to the above. Such co-registration may be achieved by constructingthe atlas relative to a template clinical image (a representative sampleor perhaps averaged image) and then performing co-registration of thetemplate. An example of this is shown in “Medical Image Registration”,Derek L G Hill et al 2001 Phys Med. Biol. 46 R1. This information can beused as further inputs and constraints to an automated trajectorycomputation algorithm, such as described previously.

Table 1 summarizes this variation across types of surgery. While it isclear that it is very desirable to avoid many anatomical features, theremay be cases where the surgeon does in fact wish to visit and passthrough an anatomical feature. Examples of these include deep brainstimulation, resection of multiple tumors, and penetration through asulcul path.

TABLE 1 Structures to be minimally impacted White Type of Blood mattersurgery Nerves Muscles Ligaments Tendon vessels tracts Cranial X X Xsurgery Endonasal X X X Spinal X X X X Orthopedic X X X X

The method also includes assigning a score to the one or more trajectorypaths to quantify how well the one or more trajectory paths satisfy thesurgical intent, and based on a comparison of these scores, the bestsurgical path is calculated. Some non-limiting examples of metrics whichcorrelate to surgical intent for general surgery on any anatomical bodypart and would be taken into consideration when calculating scoresassociated with alternate surgical trajectories are listed here:

-   -   1. For surgery involving structures such as, but not limited to,        nerves, blood vessels, ligaments, tendons, organs etc, the        surgical path's incident angle relative to an individual        structures may be used to determine the average amount of damage        expected to be sustained by the structure, where a steeper        incident angle (closer to orthogonal with the structure) would        cause more damage and therefore correspond to a worse score than        a more parallel incident angle which (closer to parallel with        the structure) would cause less damage and therefore correspond        to a better score. Further, the number of structures that are        expected to be critically intersected can be used as an        extension to the metric described herein.    -   2. The lengths of the surgical paths could also be used to score        the trajectories. For example depending on the type of surgical        device, its shape and size, longer trajectories may cause the        device to apply force over a larger area which may result in        greater trauma overall than if the path was shorter. Therefore        in this case a shorter path would correspond to a better score        whereas a longer path would correspond to a worse score.    -   3. The number of waypoints used for specifically changing        directions could also be used to score. For example if the        surgical device is rigid, the higher the number of directional        changes that occur the, and the greater the directional change        angle(s), the more the tissue is forced to deform. This        deformation of the tissue in various orientations with respect        to the surgical device may cause additional internal strain and        wear on surrounding tissue causing damage thereto. In this        manner, a higher number of directional changes, and higher        angle(s) of directional change, would correspond to a lower        surgical path score. In the case of tumor resection, the        incident angle at which the surgical path meets the tumor        boundary could also be used for scoring. As a substantially        tangential path would be more likely to have the surgical device        miss the tumor, slide off the tumor without properly cutting        into it, or cause the tumor to roll around relative to        surrounding tissue, and thus cause more stress on the        surrounding healthy tissue, it should correspond to a worse        score. In contrast, to the extent the surgical path is at an        orthogonal incident angle when it meets the tumor it will        correspond to a better score.    -   4. In other examples the organs or structures being penetrated        by the surgical path may also be taken into consideration for        the scoring of the path. In spinal surgery for example, specific        ligaments may be ideally not penetrated as they are vital for        effective functionality of the joint—the less of these ligaments        which are damaged the better the corresponding score of that        particular path.    -   5. The surgical path scores may also be weighted based on        statistical data of recovery of the patients derived from        previous surgeries within the same context. For example, after a        similar path was used (X) times to perform the specific surgery        the patient recovery rate was (Z1), in comparison to an        alternate path that was used (Y) times where the patient        recovery rate was (Z2). In an exemplary embodiment, a “similar        path” metric would identify a similar path to the proposed        surgical path to be scored, based solely on the anatomic        location of the target within a standard atlas (i.e. where a        surgeon would describe the location of the target) and the        corresponding location of the entry point based on the same        atlas. More detail could be added, based either on additional        resolution) or pathology (e.g. the type of tumor), or on        detailed statistics or metadata of the surgical path followed        (e.g. interaction with anatomical features from an atlas). Other        criteria that could be used in assessing a “similar path” would        be, the type of tumor being resected from a given path, the        specific ligament permeated from a given path, the age of the        patient, the location of the tumor, the organs/regions of        interest permeated, etc. Therefore shorter recovery time (Z)        would correspond to a better score for that particular surgical        path.    -   6. The vicinity of blood vessels to a particular path could also        be used to score surgical paths, as the fewer blood vessels        (veins and/or arteries) impacted, the lower will be the trauma        sustained by the patient. Therefore the lower the number of        vessels in the vicinity of the path the better the score.    -   7. The length of tissue being penetrated could also be used to        score the surgical paths, as penetrating through tissue is        typically much more traumatic then simply forcing it aside. In        this case, paths that require more cutting of tissue would be        given a worse score than those requiring less. In addition, the        types of tissue being cut or penetrated would also affect the        score.    -   8. Another metric would be the fragility of the tissue being        traversed, as in general highly fragile tissues are more likely        to suffer damage under manipulation than tougher tissue        structures. In this embodiment, an atlas and or a database to        derive the most likely values of the specific areas being        traversed by the surgical paths in consideration may be used, or        else, this information could be derived from direct tissue        density or elasticity measurements such as from ultrasound or MR        elastography. In yet a further embodiment, tissue fragility may        be inferred from known properties of the tissue, including        without limitation, its stiffness or rigidity.

These metrics will change depending on the surgical tool being insertedand the surgery being performed. Hence, the score presented toalternative trajectories will incorporate both the type of surgery andspecific tools that are planned to be used in the procedure. This alsoprovides the surgeon an opportunity to evaluate the pros and cons ofusing different surgical techniques and tools for a certain procedure.

The method may also include comparing the scores of the one or morepoint-wise surgical trajectory paths to a surgical intent score of apath of the shortest distance between the one or more target locationsand the closest entry point(s). It is noted that most surgeriescurrently performed presently use a straight linear path from thesurface to the target which corresponds to the shortest distance.Therefore this method performs a score comparison between the moreprominently used shortest distance surgical trajectory path and thealternate path being proposed, allowing the difference to be noted bythe user for future consideration. In some cases the straight pathapproach may give the best score in which case, which may also beconsidered by the user.

The clinician (typically a surgeon) designated location of the one ormore potential entry points and target location(s), the first target tobe approached, and the surgical outcome criteria are all inputs that maybe communicated to the computer by the clinician, and which are allstored in the computer storage device.

It is noted that the present method and system can be configured to behighly automated requiring little input from a clinician. For example,the computer processor may be programmed to determine the locations ofone or more surgical targets to be approached by comparing the image ofthe 3D volume of the patient's anatomy to an anatomical atlas and/or alibrary of stored images of normal healthy tissue, as described herein.The computer processor may be programmed to select one or more potentialentry points and then compute one or more surgical pathways to the firsttarget to be approached and then score each pathway based on a storedset of surgical outcome criteria associated with the particularanatomical part being operated on. The computer then compares the scoresand selects the pathway with the best score for the particular set ofsurgical outcome criteria.

Once the one or more surgical paths have been determined, thesurgical/clinician team may wish to run simulations so that the systemis programmed to visually display a simulation of the surgical toolapproaching the target along the one or more surgical paths andaccessing all portions of the target to be engaged by the surgicalinstrument.

Example Brain Surgery Planning Method

FIG. 5 illustrates the processing steps involved in the planning systemusing a flow chart. The first step involves acquiring pre-operativeimages of the patient (as shown in step 500 in FIG. 5). The image dataseries is first imported into the software from a database or a server,such as a PACS server. The pre-operative surgical planning method andsystem use pre-operative images (namely those images obtained prior toinitiation of the surgical procedure) obtained using at least one, orany combination of, MRI, CT, PET or similar modalities which have thenecessary tissue penetration to image the desirable parts of the brainprior to invasive procedures being initiated, and which images typicallyinclude fiducials or other markers for orienting the imaging in space.

The present planning method and system can also advantageously use morethan one imaging modality. In this situation, the images from thedifferent modalities are co-registered with each other to give combinedinformation. For example, in an embodiment, MRI may be obtained underconditions suitable to acquire both diffusion (typically DTI) data andto obtain MR data useful to generate a 3D sulcal surface map. Thesepre-operative MR images from which the diffusion images are obtained areco-registered with each other as is also done with the MR images used toobtain the 3D sulcal surface map (as shown in step 510 in FIG. 5) sinceeach MR imaging modality would have its own orientation, geometricscaling and distortions.

As discussed herein, the co-registration process (510) is a common wellknown process where appropriate transformations are applied to images sothat they match each other from a geometric point of view and henceanatomical regions overlap each other in the images obtained using thevarious modalities. One commonly used algorithm to co-register images is“PET-CT image registration in the chest using free-form deformations,”IEEE Transaction on Medical Imaging, Vol: 22, Issue: 1, (2003). Once theDTI and 3D sulcal surface map are generated, the method involvesoverlaying of the DTI data onto the 3D sulcal map data. The 3D sulcalmap is constructed using the MR data to generate a 3D surface map torepresent the brain surface and clearly illustrate the sulcal folds orcrevices that are present on the brain. The 3D sulcal map is constructedfrom the T1 MR image after removing the skull structure from theacquired image. An example algorithm for removing the skull (also knownas skull stripping) is provided in “Geodesic Active Contours,” VincentC. et. al., International Journal of Computer Vision 22(1), 61-79(1997). This overlay of the sulcal map and DTI assists in the detectionof co-registration errors since wrong DTI estimates will manifest asprotrusion of brain fiber tracts beyond the sulcal boundaries orprotrusion into the gyri. Such deviations can be quantized to arrive ata score or metric for quality of co-registration between various imagingmodalities. An example algorithm for quantizing the registration errorat this stage is as follows: ratio of length of tracts contained in thewhite matter boundary to the total length of tracts. Ideally, thismetric should be as low as possible. One minus this ratio can be used asa goodness measure for assessing the quality of DTI estimation relativeto the available brain map.

The process of scoring gives a “goodness of fit” measure between the 3Dsulcal map and the DTI data and if the score between the 3D sulcal mapand the DTI data suggests an unacceptable amount of registrationdeviation is present, remedial action will be required to improve thescore prior to completing the planning procedure. This remedial actionmay include re-estimation of tractography data (DTI) using a differentstarting region that is selected by the user or automatically selectedin the vicinity but not overlapping with original seed points. Thestarting region or collection of points is commonly used in DTIestimation to estimate voxels that collectively represent individualtracts.

A common source of error in DTI estimation is selection of a wrongprimary direction for fiber tracts going through a given voxel. Theabove described selection of a different starting point for tractestimation can force the selection of an alternate primary direction fora given voxel and hence avoid the estimation of a tract that extendsinto the sulci or beyond the brain surface. It should be understood thatDTI estimation is an optimization process and one of many commonlyavailable estimation methods may be attempted to arrive at alternatetracts and the set of tracts that are anatomically reasonable may beretained for subsequent processing. Anatomical correctness of the DTIestimation can be judged by a human reviewer or automated by a softwarealgorithm that estimates the same goodness measure described above whileutilizing additional information, such as an anatomical map of the brainthat illustrates relative concentration of tracts in known regions ofthe brain.

This approach may be complicated by the fact the presence of largetumors may geometrically distort the tracts around the tumor region. Aninventive aspect of the proposed system is that it can minimize theimpact of this distortion on the goodness measure by limiting itsestimation to the side of the brain that is least impacted by the tumor.Tumors requiring surgical intervention are often limited to one side ofthe brain. This information is known apriori since a diagnosis of thetumor(s) would have been completed prior to initiating surgicalplanning.

After reviewing the processing results using visual confirmation andevaluation of co-registration score, and in the event deviations werefound, taking the above discussed steps to obtain overlap datasubstantially free of unacceptable deviations, specific regions ofinterest can be defined on one or more images as shown in step (520).Through use of a computer interface the regions can be defined by aclinician on one more 2D image layers, and a corresponding volume ofinterest can be defined by interpolating between such defined regions.Alternatively, a specific point may be selected by the user to providean initial estimate of a region of interest (ROI) and a softwarealgorithm may be employed to identify a region in the 2D image layer. Acommon method to identify such regions is known as connected componentlabeling. This is described in detail in the following reference andComputer Vision, D. Ballard and C. Brown, that are commonly used ingraphics processing.

Alternatively, image segmentation may be employed to establish such ROI.Such ROI may be manually or automatically generated for multiple 2Dlayers and a volume of interest (VOI) may be established byinterpolating in 3D space between such ROI. Again, common techniquessuch as spline fitting may be employed here. The VOI can be visualizedwith respect to an anatomical atlas that may be overlaid on the 3Drendered MR, CT or sulcal maps. The ROI and/or VOI may act as landmarksfor lesions in need of treatment, or critical regions that must beavoided, during a surgical procedure. In the scenario where ROI or VOIrepresent a lesion or a region to be resected, the surgeon uses these astarget regions. The volume also provides an estimate of the mass of thelesion, tumor or other region that must be resected, which may be usefulto a clinician during surgery. Also, intra-operative imaging may be usedto assess reduction in volume of the target region throughout thesurgical process. In the alternate scenario where ROI or VOI representregions to be avoided, the surgeon uses these as landmarks where he/shemust proceed with caution so as to preserve these regions while stillbeing able to access pathology regions (e.g. lesions, tumors, bloodclots, etc.).

Regions to be avoided, in compliance with the desired surgical outcomeintent, may be defined as ‘no fly zones’ for the surgeon to avoid toprevent potential damage to patient's motor, sensory, or any othercritical function. Hence, ROI and/or VOI may be defined specifically fora patient based on specific function that would be desirable to bepreserved for the patient. Hence, ROI and VOI aid in defining a surgicalpath that would be uniquely tuned to specific function(s) that need tobe preserved for a patient.

Some non-limiting examples of metrics which correlate to surgical intentspecific for brain surgery and would be taken into consideration whencalculating scores associated with alternate surgical trajectories arelisted here:

-   -   1. For brain surgery, the surgical path's incident angle        relative to the individual fiber tracts may be used to determine        the average amount of damage expected to be sustained by the        tract, where a steeper incident angle (closer to orthogonal with        the tract) would cause more damage and therefore correspond to a        worse score than a parallel incident angle which (closer to        parallel with the tract) would cause less damage and therefore        correspond to a better score. A basic implementation of this        would to be take the absolute value of the cosine of the angle        between the surgical path and the orientation of the        intersecting fibre tract. By way of example, one may assign a        score of one for parallel tracts, a score of zero for        perpendicular tracts, and set a threshold score under which a        nerve fibre tract may be critically intersected. The number of        such critically intersected tracts can be used as an extension        to the metric described, as, for example, here, a path's score        can be divided by or reduced by a function related to the number        of critically intersected tracts, thus reducing the score for        such paths.    -   2. For brain surgery, the tracts that are critically intersected        by the surgical trajectory can be followed to identify regions        of brain that are connected by these tracts. Using this        information along with a brain atlas, for example, the functions        of the nerve bundles could be determined (i.e. hypothesized) and        used to score the path accordingly. In this case, functions that        are most suited to be preserved for a particular patient (as        determined by the surgeon and patient) would be prioritized and        assigned a worse score if intersected, than other neurological        functions. For example, preservation of motor function of upper        extremities would likely be prioritized over other functions for        a professional guitarist. Such regional analysis can be done by        a clinician and provided to the system as a series of        regions-of-interest or a label image.    -   3. The lengths of the surgical paths could also be used to score        the trajectories. For example in the case of port based brain        surgery, longer trajectories may cause the device to apply force        over a larger area which may result in greater trauma to the        brain than if the path was shorter. Therefore in this case a        shorter path would correspond to a better score whereas a longer        path would correspond to a worse score.    -   4. The number of waypoints used for specifically changing        directions could also be used to score. For example in a port        based brain surgery, given that the port is rigid, the higher        the number of directional changes that occur in the path, and        the greater the directional change angle(s), the more the brain        tissue will be forced to deform. This deformation of the tissue        in various orientations with respect to the port will cause        additional strain and wear on the impacted surrounding tissue,        in particular, the nearby nerves and nerve bundles. In this        manner, a higher number of directional changes, and higher        angle(s) of directional change, in the port along the surgical        path would correspond to a lower surgical path score.    -   5. In the case of tumor resection in the context of port based        brain surgery, the incident angle at which the surgical path        meets the tumor boundary could also be used for scoring. As a        substantially tangential path would be more likely to cause the        port to miss the tumor, fail to engage (slide off) the tumor, or        cause the tumor roll around relative to healthy tissue, all        requiring more movement of the port and consequently more stress        on the surrounding healthy brain tissue, it will corresponding        to a worse score. Whereas, in contrast, to the extent the        surgical path is at an orthogonal incident angle when it meets        the tumor boundary it will result in a better score.    -   6. The surgical path scores may also be weighted based on        statistical data of recovery of the patients derived from        previous surgeries within the same context. For example after a        similar path was used (X) times to perform the specific surgery        the patient recovery rate was (Z1), in comparison to an        alternate path that was used (Y) times where the patient        recovery rate was (Z2). In an exemplary embodiment, a “similar        path” metric would score paths based solely on the anatomic        location (as described by a clinician relative to a standard        atlas) of both the entry point and target point of the path.        Paths that shared both locations or nearby locations according        to the atlas would thus be considered similar. More details        could be added to make the metric more discriminating. For        example additional resolution may be added to the positions        (e.g. definition of the specific location in the individual        sulcus used for entry), or pathology (e.g. tumor type), or        detailed statistics or metadata of the surgical path followed        (e.g. percentage of white or grey matter displaced, or        interaction with anatomical features. Other criteria that could        be used in assessing a “similar path” would be, the type of        tumor being resected from a given path, the known mechanical        properties of brain tissue in the areas to be impacted, the age        of the patient, the location of the tumor, the regions impacted,        etc. Therefore, in this example, shorter recovery time (Z) would        correspond to a better score for that particular surgical path.    -   7. The vicinity of blood vessels to a particular path could also        be used to score the surgical paths, as less damage to these        vessels would clearly reduce the trauma sustained by the        patient. Therefore the lower the number of vessels in the        vicinity of the path the better the score.    -   8. In the case of the port based brain surgery the amount and        type of tissue being penetrated could also be used to score the        surgical paths, as penetrating through brain matter is much more        traumatic then simply forcing it aside. In this case penetrating        more tissue would give a worse score than penetrating less        tissue. In addition, the types of tissue being penetrated would        also affect the score. For example penetrating white matter,        given its neurological functional importance, would attract a        worse score than grey matter, as damage to grey matter is        typically less significant (for most cases) to the overall        health deterioration caused to the patient by penetration of        brain matter. Therefore when penetrating through the same amount        of gray and white matter using two different paths, the path        penetrating the white matter would give a worse score than the        path penetrating the gray matter.    -   9. Another metric would be the rigidity of the tissue being        traversed as highly rigid tissues are more likely to suffer        damage under manipulation than more flexible tissue structures.        This would require the use of an atlas and or a database to        derive the most likely values of the specific areas being        traversed by the surgical paths in consideration.    -   10. Another metric would be to include brain function as part of        the path score. Brain function can be measured using functional        MRI (fMRI) information (BOLD contrast imaging),        Magnetoencephalography (MEG), Raman Spectroscopy, or        electrophysiological measurements. Paths through regions with        high levels of function, a higher ranking of brain function        hierarchy (regional importance), or which are functionally        related to such regions, would all have a worse score.        Furthermore, paths through white matter tracts connecting such        functionally related regions would also have a worse score.    -   11.

Once regions of interest are defined, one or more targets may beidentified in the images (as shown in step 530). Targets correspond to athree dimensional location within the brain that must be accessed toresect the tumor (or lesion). It is known that to accurately spatiallylocate a point in 3D space, a minimum of three orthogonal planes arenecessary. However, additional views may be presented where theseadditional views contain images obtained using different modalities. Inother words, the additional planes may geometrically overlap with theabove mentioned orthogonal planes and present images captured usingother modalities that complement the modality presented in theaforementioned three orthogonal planes. For example, the threeorthogonal planes may represent T1 MR image slices while additionalviews may present co-registered images obtained using CT or BO (anotherMR data representation). The complementary modalities aid in confirmingthe location and extent of tumors or blood clots. Another redundantmeans of presenting information to aid in estimation of tumor locationis presentation of data as radial slices where virtual slices aregenerated such that the slices are along the planes that are situatedradially about a user-defined axis.

Visualizing the target being operated on in multiple 2D images mitigatesthe risk inherent in placing the target in 3D space only using a 3Drendering of the brain (see FIG. 7). This latter approach is prone toerrors because the 3D surface is rendered on a 2D display. It should benoted that a 3D holographic display may also be used to overcome thisrisk since the surgeon will have the ability to view the 3D virtualobject from multiple perspectives to confirm the location of the target.In an embodiment, this can be used as an alternative to presenting imagedata in three orthogonal planes.

Another inventive aspect of the invention is the ability to visualizewhite matter tracts that are in the immediate vicinity of the target.This functionality is achieved by hiding diffusion tracts (ortractography information) in all regions of the brain except for thetracts that intersect the geometric space occupied by the target regionor within the immediate vicinity (within a threshold). Alternatively,the tracts that intersect the geometric space occupied by a surgicaltool that is virtually inserted in the brain may be displayed. Such toolmay be a virtual representation of a biopsy needle, a port for minimallyinvasive surgery (e.g. an access port), a deep brain stimulation needle,or a catheter, to name a few. This approach of selective display of DTIinformation helps manage the large-data problem associated withvisualization of an entire DTI image. It also aids the surgeon innarrowing their focus and seeing principally the impacted tracts, asopposed to all tractography information associated with the entirebrain. This selective filtering of renderings of white matter tracts inthe immediate vicinity of the target region, or those which are expectedto be impact by a tool, will allow the surgeon to view tract informationwithin a selectable degree of translucency in order to aid in theselection of surgical paths which may best meet the surgical intent.Furthermore, such selectable display of DTI information could similarlybe replaced or supplemented with any other co-registrable modality,including fMRI or other modalities which are able to assess brainfunctionality that may be potentially impacted during tumor resection.See FIGS. 8 and 9 for illustration of tract intersection visualization.

The system may be programmed to provide a histogram analysis, in whichthere is computed a histogram of the number of fibers that would bedisplayed versus the threshold shear cut-off angle. This providesinformation on the sensitivity to this threshold. In one embodiment, thesoftware could suggest an alternate cut-off angle near the set cut-offif there is a value where the number fibers that would be displayedsuddenly jumps, i.e., where there would be a big change in display givena small change in cut-off threshold.

Alternately, instead of a binary cutoff threshold the display could bemodulated so to provide a gradation of fibers displayed (e.g. byreducing fiber intensity or increasing transparency) as the intersectingangle increases beyond the set threshold or between minimum and maximumset thresholds.

Another embodiment may involve distance analysis where the system andmethod are configured to display only a set distance of each tract fromits intersection with the port rather than the full path of the tract,as fibers that are further from the intersection point are less likelyto be impacted. This distance threshold can be adjusted and manipulateddynamically. The display of each tract can also be modulated by distancefrom port intersection (e.g. by decreasing brightness, changing color,increasing transparency or decreasing displayed tract thickness withdistance). Alternately, the displayed tracts can be similarly modulatedby the distance of intersection with port to an end-point, as tractsthat are impacted at or near their end-points are potentially lessaffected than tracts impacted further along their trajectories.

The next step in establishing a surgical path is the identification ofthe entry point, which is also known as an engagement point (as shown instep 540). It is noted that this entry point refers to the entry pointof the leading section of the surgical port tool into the dura of thebrain. There may be another entry point of the surgical port into thewhite brain matter. The first entry point mentioned above is establishedby visualizing the sulci with the overlay of a virtual access tool, suchas a port tool, biopsy needle, catheter etc. However, an advantage ofthe current invention is that the virtual port tool may presented insuch approaches in an unobstructed manner by representing it as atranslucent model of the tool.

The target and the engagement points can be then used as navigationalbenchmarks to define a sulcal path (as shown in step 550). In anembodiment the present method and system is configured to define apiecewise linear sulcal path that includes the engagement and targetpoints as the two extreme beginning and end points respectively in thesurgical path and additional spatial locations between the two extremepoints. These additional spatial location points may be inserted todefine a piecewise linear path when turns are observed in the sulci. Thepiecewise linear path that closely follows the turns in the sulci mayoptimally preserve the regions of the brain that are contacted by thesurgical tool where such surgical tool is of low profile, and/orflexible or articulated. Hence, an articulated or flexible port can beanticipated to utilize such piecewise linear path to further reducetrauma to the brain. A metric or score can be associated with a specificsulcal path to indicate the extent of brain tracts that are intersectedby the virtual port. Hence, the score can be used as a measure of traumaexpected to be introduced by the port when using the planned sulcalpath. In other words, the number of intersected tracts may be used tocompare two or more different paths to identify the path that presentsthe minimal number of tract intersections. *

Finally, alternative location and geometry for craniotomy can beevaluated by modelling surgical tools and assessing the range of motionavailable for each tool when the tool's motion is constrained by thedimensions and location of the craniotomy (as shown in step 560). Thisrange of motion may be seen in FIG. 10. Further, the craniotomy locationand the sulcal path can be more accurately visualized by radiallystacking the image slices. In other words, the 3D reconstructed MR imageof the whole brain can be used to make virtual 2D image slices thatshare a common axis that is reasonably close to the planned sulcal path.Such slices expose the extent of sulci close to the planned path andhence assist in better visualization of alternative sulcal paths. Afinal scorecard is created to present all the metrics from each of thepreceding stages and a metric to represent goodness of fit for each ofthe defined sulcal paths. The goodness of fit for the sulcal path (alsoknown as sulcal correspondence percentage) is the ratio of the plannedtrajectory and the sum of total length of the described sulcal path plusthe Euclidian distance from the end of the path to the target. Thisratio is then multiplied by 100 to express the ratio as a percentage.This metric indicates the correspondence between the linear trajectoryand the chosen sulcal path. One hundred percent means perfect match orlinear path.

The established surgical plan is then stored and/or exported to anavigation system (570) that can typically receive such data and storeand/or co-register (if necessary) such plan or surgical path for thesurgeon to use in navigating his or her surgical tools during thesurgical procedure. An inventive feature of the planning system allowsthe surgeon to visualize the entire procedure and compare alternativesurgical plans by automatically playing back the surgical steps as avideo. This aids the surgeon in visualizing the entire procedure andhence serves as a confirmatory step and as a training step for thesurgeon.

If the medical procedure is to address a dire medical emergency andthere is no time to obtain images from multiple imaging modalities, thenthe present method and system may be configured to use a singlenon-invasive imaging modality. In this situation the planning method forplanning a pathway from a sulcus to a location in a patient's brain tobe operated on includes acquiring pre-operative images of a patient'sbrain to be operated on using a non-invasive imaging modality andco-registering the pre-operative images. The co-registered images areused to identify a sulcal structure of the patient's brain and one ormore targets and associated one or more target locations to beapproached and operated on during the invasive surgical procedure. Theone or more target locations may be visually displayed in at least threeorthogonal planes to confirm the location of the one or more targets in3D space. Based on the location of the entry point and the one or moretarget locations, there is defined a piecewise linear surgical path withthe location of the entry point and the location of a selected one ofthe one or more target locations being designated as beginning and endpoints respectively in the surgical path. The surgical path is selectedto avoid passing through selected anatomical features of the brain.

After the planning stage has been completed and the surgery has started,and once the brain tissue is visible, other imaging modalities thatcould not be used to acquire intra-operative images may then be used toacquire intra-operative images in addition to the above mentioned MRI,CT and PET modalities. Such modalities include OCT, PS-OCT, ultrasoundetc. These will be discussed in more detail hereafter during discussionof the navigation part of the surgical procedure.

FIG. 2 shows an embodiment of the present method and system, for use asan intra-operative multi-modal surgical planning and navigation systemand method. The system and method can be used as a surgical planning andnavigation tool in the pre-operative and intra-operative stages. Personsof skill will appreciate that the data input(s) of the surgical planningsteps and surgical procedures described in FIG. 1, can be used asinput(s) to the intra-operative navigation stage described in FIG. 2,through the use of patient fiducial markers visible in the imaging, orother imaging techniques, examples of which are known in the art.

The embodiment of FIG. 2 provides a user, such as a surgeon, with aunified means of navigating through a surgical region by utilizingpre-operative data input(s) and updated intra-operative data input(s).The processor(s) of system and methods are programmed withinstructions/algorithms 11 to analyze pre-operative data input(s) andintra-operative data input(s) to update surgical plans during the courseof surgery. For example, if intra-operative input(s) in the form ofnewly acquired images identified a previously unknown nerve bundle orbrain tract, these input(s) can, if desired, be used to update thesurgical plan during surgery to avoid contacting the nerve bundle.Persons of skill will appreciate that intra-operative input(s) mayinclude a variety input(s) including local data gathered using a varietyof sensor(s).

In some embodiments, the system and methods of FIG. 2 may providecontinuously updated intra-operative input(s) in the context of aspecific surgical procedure by means of intraoperative imaging sensor(s)to validate tissue position, update tissue imaging after tumor resectionand update surgical device position during surgery.

The systems and methods may provide for re-formatting of the image, forexample, to warn of possible puncture of critical structures with thesurgical tools during surgery, or collision with the surgical toolduring surgery. In addition, the embodiments disclosed herein mayprovide imaging and input updates for any shifts that might occur due toneedle deflection, tissue deflection or patient movement as well asalgorithmic approaches to correct for known imaging distortions. Themagnitude of these combined errors is clinically significant and mayregularly exceed 2 cm. Some the most significant are MRI baseddistortions such gradient non-linearity, susceptibility shifts, eddycurrent artifacts which may exceed 1 cm on standard MRI scanners (1.5 Tand 3.0 T systems).

Persons of skill will appreciate that a variety of intraoperativeimaging techniques can be implemented to generate intra-operativeinput(s) including anatomy specific MRI devices, surface array MRIscans, endo-nasal MRI devices, anatomy specific US scans, endo-nasal USscans, anatomy specific CT or PET scans, port-based or probe basedphoto-acoustic imaging, as well as optical imaging done with remotescanning, or probe based scanning.

FIG. 3 shows an embodiment of the present method and system forpost-operative data analysis. As shown in FIG. 3, the input(s) andoutput(s) 1 captured during the pre-operative and intra-operative stagesof the methods and systems described herein, may be used for analysis offuture surgical procedures and training purposes. Vast amounts of datacaptured during pre-operative and intra-operative stages can be used forfuture surgical procedures and training purposes.

In such an embodiment, the system may include dedicated database(s) 2for storing and retrieving input(s), output(s) and processor(s)activities. The database 2 may include data for recovery analysis,outcome assessment, therapy planning, pathology correlation 3, futuresurgical plans and/or training 4 and cost validation (health outcomes v.economic metrics).

Persons of skill will appreciate that the input(s) and output(s)captured by the system and method may include data on the use ofsurgical tools, continuous recording of tissue during surgicalprocedures using local, imaging scans, local raman spectra, localanisotropy information of tissues to illustrate morphologicalstructures, local hyperspectral image data of tissue to aid in tissuedifferentiation, spatial location of resected tissue for correlationwith specific regions in the body, and pathology information inferred bya pathologist or radiologist for aiding future surgical procedures ortraining purposes.

The information accumulated during the pre-operative and intra-operativestages can be effectively utilized for future surgical planning for thesame patient, gathering clinically relevant information forpre-operative surgical planning for other patients and/or trainingpurposes as illustrated in FIG. 3.

As the systems and methods disclosed herein may generate a large volumeof data to be captured, in some embodiments, input and output data maybe communicated to additional system components, for example, for remotereview of the data by users located at remote locations.

In further embodiments, surgical procedure and clinical criteria,selected for example on a patient by patient basis, can be utilized asadditional input(s) metrics to assess optimal surgical plans. Additionalmetric input(s) can include minimal trauma trajectory to location ofinterest, such as minimized vessel trauma, minimized nerve bundle traumaor prioritized nerve bundle trauma. Metric input(s) can include, forexample measured or predicted trauma to brain tissue, including damageto white matter, damage to regions connected by white matter, damage toregions of the cortex connected by white matter, and damage to vesselson approach.

In some embodiments, input metric(s) may include angle of contactbetween tissue and instruments as well as trauma to nerves and connectedfibers, which may be measured by interception or displacement of tissuewith instruments from both historical and surgical data.

Additional input metric(s) may include: position of device to be trackedrelative to tissue of interest by tracking technologies; geometry ofsurgical devices and ports; anticipated positioning of instruments andports during surgery; best practice locations for immobilization ofpatient anatomy, such as the head band region for the Mayfield clamp;and locations for associated procedures, such as the administration oflocal anesthetic. Persons of skill will appreciate that input metric(s)can be associated with particular approaches, diseases or procedures,and that these metrics can be both user selected and automaticallygenerated.

In further embodiments, processor(s) may be used to perform imagingartifact, or anomaly, correction to represent structures and targets inaccurate positions. Gradient non-linearity correction, susceptibilityshift correction, eddy current artifact correction and pixelinterpolation error corrections are examples of processes that may beperformed by the method and systems to correct for artifacts in theimages, post-acquisition, and to provide high quality and accuraterepresentations.

In still further embodiments, the systems and methods may includeco-registration components and techniques to align various imagingmodalities and varying scans within a modality. Registration mayperformed on images acquired by numerous types of sensor(s) includingMRI, PET, CT, US, Optical imaging, such as surface scanning andspectroscopic techniques and photo-acoustic imaging.

In still further embodiments, the systems and methods may be configuredto direct sensors to particular regions of interest in the patient'sbody, to produce high quality images intra-operatively that focus onspecific areas of interest, specifically, the area of the desiredsurgical field or point at the appropriate time during the surgery.Implementation of such surgical field imaging may be achieved by thesystem through the use of appropriate scale of imaging or contrastmechanisms, for example. By focusing the imaging on a specific locationof interest, the signal to noise can be improved by multiple factors,and new imaging contrast mechanisms can be utilized.

In some embodiments, the system and methods may generate as output(s)minimally invasive approaches, based on the processor(s) analysis of theinput metric(s). For example, input metric(s) may be weighted togenerate patient or procedure specific output(s). The processor may torank various surgical alternatives presented by a surgeon or varioussurgical alternatives may be automatically generated by the system usingadaptive learning paradigms, such as decision trees and neural-networks.

In some aspects of the present methods and systems, there is providedsystems and methods to integrate surgical instrument and port specificinformation, such as size, shape or impact on nervous tissue with dataon the patient's anatomy to qualify user selected port approaches. Forexample, input(s) including properties a subject's nerve fascicles,nerve bundles, sulci and gyrus patterns, vessels, skull and skull-basecan be used to assess the surgical instrument or port insertion's impacton the nervous structures of the brain. In some embodiments the systemsand methods can provide for surgical instrument and port planning todetermine an appropriate craniotomy, incision, head-holder, externalimaging devices and location of equipment in the operating room based.These systems and methods may lead to less invasive, more accurate andfaster insertion device or port based surgical procedures, withimprovements to patient and economic outcomes.

In some embodiments, the systems and methods disclosed may include asinput(s) data on the fibers, sulcus and gyrus structures of the brain,in addition to other input(s) such as tumor location. These input(s) maybe useful in determining paths or locations of surgical deviceinsertion, for example. In some embodiments planning output(s) mayinclude device insertion paths into the brain through natural orificessuch as sulci. In other embodiments, input(s) such as tumor databases inaddition to other input(s) such as tumor location, can be included.

In some embodiments the systems and methods can include tractographyinput(s). In the system and methods described herein, thedifferentiation between tumor and healthy tissue may be performed withDWI sensor(s) and associated processor(s) which use the diffusion ofwater through tissue of the brain, by Brownian motion, as the primarytissue contrast mechanism. The data acquired from the diffusion contrastscan can be acquired in a predefined gradient direction to enablevisualization of diffusion along a specific direction in the brain,represented in FA maps that provide information about the generaldirectionality of diffusion throughout the image. The processor(s) canuse this directional information to generate connectivity maps definedby sets of vectors to generate fiber tracts in the brain; wherein thesetracts correspond to water diffusing on the outside of the white mattertracts through the brain and correspond to the major nerve fibers in thebrain.

For example, the systems and methods may include diffusion contrastimaging devices to generate DTI images, and measure the FractionalAnisotropy (“FA”), and Apparent Diffusion Coefficient (“ADC”) of tissue.The ADC, which measures the magnitude of diffusion, and the FA whichmeasures the general directionality of diffusion throughout the image,can be used to identify major fiber tracts through the brain, measureincreased cellularity associated with tumors, measure diffuse or localtraumatic brain injury and white matter disease associated withneurodegenerative disorders.

Through the combination of ADC, FA maps and DTI images the systems andmethods can measure major fiber tracts through the brain, measureincreased cellularity associated with tumors, measure diffuse or localtraumatic brain injury and white matter disease associated withneurodegenerative disorders. For example, to perform a craniotomy toresect as complete of a tumor margin as possible, the multitude of MRIcontrast mechanisms can be used to define tumor boundary, definecritical structures in the brain, define functional areas, and define anapproach to tumor resection.

FIG. 4 shows an output(s) of an embodiment of the present method andsystem, wherein processor(s) have identified fiber tract bundles foroptimal selection of surgical approach. In the embodiment shown,output(s) may include locations and visualizations of trans-sulcipathways which may provide for avoidance of blood vessels and fiberbundles. The output(s) may visualize and track surgical approaches tominimize white matter and grey matter insertion damage.

In some embodiments, the methods and systems disclosed herein mayinclude as input(s) ranking information of fibers and tissues.

In some embodiments, the current systems and methods are configured toidentify minimally invasive corridors, for example through sulci, basedon input(s) such as the sum total of all of the white and grey matterinformation available by the system, which may be used to calculate aminimally invasive pathway. For example, given an MRI T1 image withsegmentation into white matter, grey matter, sulci, and CSF, etc., and asurgical entry point and a target specified within the information, thesystem specifies a lattice of the image voxel centers and forms a graphof the 27-connected direct voxel neighbors. Each connection is given aweight based on the voxel label as white matter, grey matter, sulcus, orother. Weights are chosen to reflect the relative preference ofimpacting one tissue type over the others (which may be determined by aclinician). A path finding algorithm (e.g. A* search algorithm, as notedabove) may be used to determine the path of least total impact totissue. Further embodiments may represent the surgical instrument(s) ina realistic manner relative to, and interacting with the representedtissues, and to represent the biomechanical properties of tissue tosimulate tissue distortion, as each path is attempted. Furtherembodiments may integrate additional imaging and outcome information tosupport clinical decision making for the approach.

The system and methods may generate and plan a minimally invasivecorridor through several different embodiments, such as 1) planningwithout using a deformation model, 2) planning using a deformationmodel, or 3) planning using intra-procedure imaging to updateinformation, in the context of a deformation model. An exemplary methodof producing a deformation tissue model is disclosed in copending PCTPatent Application Serial No. PCT/CA2014/050243 entitled SYSTEM ANDMETHOD FOR DETECTING TISSUE AND FIBER TRACK DEFORMATION, which isincorporated herein in its entirety by reference.

In an embodiment, the system and method may be configured to function onthe assumption that the tissue will not deform when the port is insertedinto the tissue. The system and method in this embodiment may beconfigured to generate minimally invasive corridor outputs with a fixedset of imaging data. Clinically, although this may be a reasonableassumption, during port surgery for example, a port will generallyfollow the sulci, and the sulci will pull, or compress the underlyingtissue.

To generate and plan a minimally invasive corridor, the system andmethods are configured and programmed to select a target of interest,which may be represented as an overlay of contrast uptake information,diffusion weighted maps (ADC), T2 changes, or a combination of these andadditional contrast, for instance. This target may be a user input, forexample, or can be generated by the system and method based on existingdata or images. When the system and method have identified the target ofinterest (such as a point on 3D image set), a representation of theaccess port that is selected by the user may be shown on an output orfeedback component of the system and method, such as a screen.

During port surgery, for example, the system and method may fix the portinto position, for example at the tip of the lesion, which may berotated around the point in three-dimensions. The line of entry of theport and its axis of insertion define the approach taken into the brain,subject to the system and methods selecting a corridor wherein entryoccurs on a single linear trajectory, not multiple linear trajectories,or curved trajectories.

The systems and methods disclosed herein may provide for virtualinsertion and removal of the port, or other surgical tool, into thebrain tissue. As it is inserted, the registered set of DTI tracks thatmake contact with the tip, and outer surface of the port may beidentified by the system and method through a ray-tracing, or similarcalculation. If the fibers come into contact with the port at an angleof 90 degrees, the system and method may predict that these are the mostat risk of shear or tear from contacting the port; however, if they runparallel, the system and method may detect that they are at the least atrisk of shear or tear. In some embodiments, the system and method mayset a threshold (for example, an angle of over 60 degrees) which maysuggest damage to the nerve fiber. This threshold can be modified by thesurgeon in practice, and when set, may allow for an inference of allnerve fibers that are at risk during a procedure.

Further embodiments can provide visualization tools to assess theeffects of different potential shear angles between intersected fibersand the inserted port. These tools can include the display of a computedhistogram of the number of fibers that would be at risk of shear versusthe shear cut-off angle. Such tools can provide information on thesensitivity to this threshold. The embodiment can also be configured tosuggest an alternate cut-off angle near the set cut-off if there is avalue where the number of displayed fibers suddenly jumps—i.e. wherethere would be a big change in display given a small change in cut-offthreshold. Alternately, instead of a binary cut-off threshold, theembodiment can also be configured so that the display could be modulatedso that there is a gradation of fibers displayed (e.g. by reducing fiberintensity or increasing transparency) as the intersecting angleincreases beyond the set threshold or between minimum and maximum setthresholds.

A further embodiment can show only a set length of each fiber tract fromits intersection with the port rather than the full path of fiber, asfibers that are further from the intersection point are less likely tobe impacted by the port insertion. This length threshold can be adjustedand manipulated dynamically. The embodiment can also be configured tohave display of each fiber modulated by distance from port intersection(e.g. by decreasing brightness, changing colour, increasing transparencyor decreasing displayed fiber thickness with distance). Alternatively,the display fiber can be similarly modulated by distance of intersectionwith port to a fiber end-point, as fibers that are impacted near theirends are potentially less affected than fibers impacted further alongtheir trajectories, in other embodiments.

To provide a user with a visualization of nerve fibers within thecontext of the rendering volume, the system may outline the impactednerve fibers in black, such that the black lines can be projectedthrough the 3D rendering, for example. In addition, the system maydisplay a thin slab of the rendered DTI data volume, such that this slabmay be moved along the axis of the port on the output devices to displaythe impacted fibers at various depths along the port. In addition,looking coaxially down the port, for example, all of the fibers thatcontact the port may be shown as a rendering on the output devices ofthe system and method.

Furthermore, as a means to reinforce a port based approach, the systemand method may represent the port as a fixed visualization mode, suchthat the brain and tissue beneath the port may be moved relative to theport, on an output device or display. This may provide a visual means offinding an appropriate, least impactful path to the point of interest.

Additionally, the system and method may identify a frame of reference atthe targeted point within a target, such as a tumor. This may provide auser with “inside looking out” view which may be beneficial forvisualization of possible pathways to the tumor by identifying openingsthrough the path to the surface. This may be used as an alternative orcomplementary means of the system and method's use of rendering toidentify a path.

In some embodiments, the system and method may model a surgicalinstrument, such as a port, as a larger or smaller diameter in order todetermine whether a different port size can be used for a particularprocedure, or the sensitivity of an approach to variations in theprocedure, such as mis-registration of data-sets, in-accuracies withnavigation of the port, or movement of tissue during the procedure. Inaddition, the port target point may be shifted by the system and methodto determine the impact on the sensitivity of the approach.

In some embodiments, in addition to finding the least impactful approachrelative to the fascicles and nerve bundles, the system and method cantend identify the sulci as a preferred access route to the brain. Insuch embodiments, the surface rendering of the tissue may be used by thesystem and method to identify these natural orifices. This may constrainthe output trajectories to only those trajectories that insert at sulciat the surface of the brain.

In addition, the system may provide for overlays of veins, viable greymatter, and arteries, presented relative to an approach. From thisinformation, the impact of an approach can be better assessed. Forinstance, the system may calculate the total volume, or number, orlength of fiber tracts that may intersect the port at a given point, oralong a given trajectory. This can be expressed by the system and methodas a total number (such as a histogram for example) may be weighted inorder to express a pre-defined, or a user input hierarchy for nervebundles and fascicles. In some embodiments this calculation can also bemade by the system and method with respect to blood vessels in thebrain, or with respect to major fiber bundles, or banks of tissue thatare critical such as the motor strip. The distance and the angle that asurgical device, such as a port makes to the bank can, in someembodiments, be calculated as an additional metric. Major fiber bundlesthat the system may apply this processing to may include the coronaradiata, or optic chiasm, as some non-limiting examples.

In some embodiments, the system and methods can also use inputs on thegeneral orientation of the fiber relative to the patient frame to weightfiber bundles. For example, the systems and methods may assign differentweightings to fibers that can be calculated in the sum total of theimpact of the trajectory. In some embodiments, the hierarchy for fiberscould be color weighted, such that fibers assigned the color red wouldbe dominant those assigned the color blue, and fibers assigned the colorblue would be dominant to those assigned the color green. In otherembodiments, the system and method may use color on the rendering todefine fiber orientation relative to the port. For example, fibers thatare substantially perpendicular to the port may be colored as red, whilefibers that are within the tolerance of damage could may be coloredblue, and fibers that are outside the tolerance of damage may be coloredgreen. Alternatively, in some embodiments, a fractional an-isotropy mapmay be used by the system and method to represent fiber connectivity,such that colors attributed to such a representation could be scaled tocorrespond to the weighing of fibers.

In some embodiments, the system and methods may select a minimallyinvasive path which tends to follow a sulcus to the lesion of interestand deforms the sulcus as minimally as possible. In such embodiments,the system and method may determine the total distance from a sulci fora given port trajectory, which may be expressed for example as anintegrated distance along the port, or a total amount of deflectionrequired to align a port path to a sulci. When measuring a sulcusapproach, the total amount of grey or white matter traversed tends to bea critical metric of the system and method. This may be calculated bythe system and method from 3D models, and displayed as measurements inmillimeters, or other units, or for example, as a ratio of grey matter,white matter and sulci traversed. In some embodiments, the system andmethods may associate different weightings to different types of tissue(for example grey matter, white matter and sulci), as well as thefascicles impacted. This may be calculated from the port position, butin some embodiments may be measured with additional inputs accountingfor the displacement of the sulci when the port is inserted, and thesulci follows the outer contours of the port.

In some embodiments, the system and method may process inputs on thebasis that the introduction of a surgical access port, and anintroducer, for example, will tend to displace a significant amount oftissue internally, as well as displace the folds of sulci as it ispushed into the brain. For tissues that are stiffer than the surroundingbrain tissue, for instance some clots/hematomas, cellular tumors, thesystem and method may account for the expected internal shift of tissueas the introducer pushes against the tissue. This displacement may bepredicted or measured for example by the system and method with accuratesimulation, using apriori tissue stiffness information, geometricknowledge of an introducer and port, a biomechanical model of tissuedeformation, (using the skull as a boundary condition, the port as aboundary condition) and using pre-operative imaging data. In someembodiments, the user may modify numerous variables for modeling, suchas relative stiffness of a tumor and surrounding tissue as disclosed incopending PCT Patent Application Serial No. PCT/CA2014/050243 entitledSYSTEM AND METHOD FOR DETECTING TISSUE AND FIBER TRACK DEFORMATION,which is incorporated herein in its entirety by reference.

User or system and method implemented changing these values, allowingfor visual outputs relating to how the tumor may move within the brainvolume may provide a good sensitivity analysis for an insertion approachto be taken. In some embodiments, the stiffness can be predicted basedon T2, diffusion and contrast information, however it can also bemeasured directly from elastography imaging (ultrasound, MRI or OCT, forexample).

In some embodiments the system and method may process inputs andgenerate outputs based on the concept that the sulcus in contact with aport will deform the surrounding sulci to match the surface of the port.The system and method may model this interface using a biomechanicalmodel wherein the sulcus tissue will be at a sliding boundary interfacewith the port. As the diffusion fibers, and blood vessels that areattached to the surface of the sulci, typically terminating at the endsnear the surface of the brain, and running more parallel lower, willtend to track with the sulci, another boundary condition processed bythe system and method may be that the fibers track with the sulcidisplacement. The network of fibers can then be used as registrationpoints and act as connections as part of a 3D network with their ownstress and strain profiles. The global deformations of the brain may bemodeled by the system and method using continuity of the sulci, vessels,and major structures.

The system and method may update this process and model using real-timeimaging information input(s) as the introducer is positioned inside thepatient, for example, the patient's head. In some embodiments thereal-time imaging may performed using an in-situ port. For instance,real-time ultrasound imaging performed on the tip of the port, maydetect tissue stiffness inside the brain. This information can be usedby the system and method instead of the priori-predicted stiffness, andcan provide an estimate of tissue movement. In addition, ultrasound maybe used to identify sulci patterns as a port is being introduced into apatient. These actual sulci patterns may be matched by the system andmethod to pre-operative sulcus patterns, and a deformed pre-operativemodel may be generated based on this information. In this iterativemanner, the model will be updated by the system and method according toinformation obtained during the procedure to provide for accuraterepresentations of the tumor location, for instance modeling of tumorroll within the brain, and also the ability to measure the total stressand strain on nerve fibers as the port is inserted into the brain. Thismay be represented by the system and method as a global value and aswith the weighting of the hierarchy of the fibers, the actual strain ofthe fibers may be used to calculate a value associated with theinvasiveness of a surgical approach.

In some embodiments, the system and method disclosed herein may be usedto better model the proposed movement of a surgical device, such as aport within a patient's body, such as their tissue, to allow for removalof a tumor that is larger than the opening at the end of the port. Inthis embodiment, sweeping of the port to access all boundaries of thetumor may modeled by the system and method based on the fixing of theport at the surface of the brain. For example, when the port is movedthrough different locations of the tumor, the movement of the port maydisplace the fibers, and the biomechanical model can be used to measurethe stress and stain profile across the fibers in the brain as discussedpreviously. In some embodiments, the system and method may includeadditional strain gauges located on the outside of the port to measurethese effects in-real-time. These values may correlate with the planningmodel of the brain, and indicate to the surgeon when they aredis-concordant or when a tolerance threshold that is pre-determined hasbeen exceeded.

Additionally, as the port is moved, tissue may be removed in volumeindicated by the surgeon. The biomechanical modeling components of thecurrent system and method would then calculate the new tissue positionthrough the local volume. Additional real-time imaging may be performedby the system to validate the new tissue boundaries. For example, ifreal-time imaging with navigational positioning information isavailable, such images can be compared with the estimated position ofthe calculated tissue. Such comparison can be done directly if similarcontrast is used in both cases, or in a mutual-information sense if thedata is not directly comparable. The system can then report the qualityof agreement between the new data and the estimated tissue positions.Further still, in some embodiments, the system and method may includerobotic or semi-robotic manipulators for use in a similar context. Theinput to the robot may be strain gauge metrics measured directlyin-vivo, and/or using in synchrony with stresses and strains predictedin the surgical planning model. The ability of the system and method tomeasure fine stresses and strains may be useful in surgical interventioninvolving other brain injuries and diseases such as TBI (traumatic braininjury), Parkinson's, Multiple Sclerosis (MS), and Alzheimer's disease.

In embodiments, there is a system comprising of a computer or processingsystem, pre-operative images from various modalities (MRI, CT, PET,etc.), a tracking or navigation system (optional in case of planningsystem), a single or set of input devices (keyboard, touch screen,mouse, gesture control, etc.), a single or set of output devices (amonitor, a laser pointer, etc.), pointers or tools that act as pointingdevices, (optional in case of planning system), tracked surgicaldevices, such as, scissors, ablation devices, suction cutters,bi-polars, (optional in case of planning system), tracked access portdevices and guidance guided (such as automated, semi-automated ormanually positioned with alignment feedback) external imaging system (tofacilitate delivery of external imaging modalities, aligned to deliverimaging through the access port devices). The system can be used as asurgical planning system, i.e. wherein intra-operative guidance andintra-operative imaging is not part of the system; or as a combinedplanning and intra-operative guidance system where information collectedduring the surgical procedure is used to guide next surgical steps, ormeasure predicted patient outcome.

In some embodiments, the present system may include surgical simulationcomponents, for example robotic systems with haptic feedback. In someembodiments, the simulation features provided by the system and methodsdisclosed herein can also incorporate a phantom that can be used fortraining and planning of a specific surgical procedure, as well asimaging of the phantom. An example of how to make a brain phantom forboth imaging and biomechanical training of the brain of the patientbeing operated on is disclosed in United States Publications No. XXX andPublication No. YYY which correspond to U.S. Provisional PatentApplication Ser. No. 61/900,122 and Ser. No. 61/845,256, respectively,which are incorporated herein by reference in their entirety.

Features of this phantom may include: texture closely mapping the humanbrain such that insertion of the surgical port along the sulci can bepracticed; anatomically correct brain structure to closely emulate thespecific patient's brain which can be established by methods such as MRIwell in advance of a surgery; emulation of the presence of a tumor ofthe right type and at the right location in the phantom (for example,the tumor can be identified a priori as soft and free flowing or highlycellular and dense. This information may be incorporated in the creationof the simulated brain to closely match the placement of a tumor to theinformation inferred from pre-op imaging modalities and to allow thesurgical team to evaluate the specific surgical procedure and approachin the context of the specific patient); emulation of the presence ofblood vessels with for example, colored fluid to emulate vein structureimmediately below the scalp; and emulation of the presence of skull anddura through, for example, the use of a mouldable rigid material such ascast material. The durum may be emulated through the use of polymersheets that are thin and have substantial durometer such that thesynthetic dura displaces during the surgical opening step. The presenceof synthetic skull may enable the surgical team to practice opening of acranial port during a simulation of the craniotomy.

Persons of skill will appreciate that in all methods where aquantitative approach is used to calculate trajectories for portpositions, an algorithm may be used to calculate a ranked set oftrajectory paths that a user can select from. The user, such as asurgeon, may search these options based on differing criteria such asminimizing global fascicle involvement, minimizing vessel involvement,or minimizing total nerve fiber strain.

Further, in some embodiments, once a trajectory has been selected, thesystem and method may search a database of prior cases for similartrajectories used in the context of, similar tumor sizes, locations, andDTI fiber map tracts. The outcomes associated with those approaches maybe compared by the system and method, and may be presented so as toimpact trajectory selection. In some embodiments, actual intra-operativedata could be referenced, for example strain measurements in vivo, orDTI maps post-operation.

In use, the systems and methods of this disclosure may be used forsurgical procedures wherein there is a need to spare critical structuresthat can be imaged using pre-operative or intra-operative imagingmodalities. The surgical planning aspects of the present method andsystem may be useful in minimally invasive access procedures includingport based neurosurgical procedures and endo-nasal approaches such ascorridor based procedures, endo-nasal procedures, port based procedures(rigid fixed diameter), tumor resection, stroke tissue resection andreperfusion, ICH vessel clipping, biopsy via sulci, stem cell recovery,DBS system delivery, catheter based (flexible, smaller diameter).Although the systems and method described herein have used port basedsurgery, and surgical tools, as examples, the scope of this inventionshould not be limited by the embodiments set forth in the examples, butshould be given the broadest interpretation consistent with thedescription as a whole.

The systems and methods described herein may be used in applicationssuch as spinal surgical procedures, tumor resection, disk repair,alignment of tendons, pain management, functional device implantation,neck or sinus surgery, functional surgery, cardiac or pulmonary surgery,cardiac function, lung cancer removal, removal of clot or diseasedtissue, body cancer or colon imaging, polyp removal, liver, prostate,kidney or pancreas imaging. Persons of skill will appreciate that themethods and systems described herein are not limited to the uses andsurgical procedures described above, but can be extended to a variety ofprocedures that utilize imaging, planning and navigation.

At least some of the elements of the systems described herein may beimplemented by software, or a combination of software and hardware.Elements of the system that are implemented via software may be writtenin a high-level procedural language such as object oriented programmingor a scripting language. Accordingly, the program code may be written inC, C++, C# SQL or any other suitable programming language and maycomprise modules or classes, as is known to those skilled in objectoriented programming. At least some of the elements of the system thatare implemented via software may be written in assembly language,machine language or firmware as needed. In either case, the program codecan be stored on a storage media or on a computer readable medium thatis readable by a general or special purpose programmable computingdevice having a processor, an operating system and the associatedhardware and software that is necessary to implement the functionalityof at least one of the embodiments described herein. The program code,when read by the computing device, configures the computing device tooperate in a new, specific and predefined manner in order to perform atleast one of the methods described herein.

Thus, while some embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that various embodiments are capable of beingdistributed as a program product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer readable media used to actually effect the distribution.

A computer readable storage medium can be used to store software anddata which when executed by a data processing system causes the systemto perform various methods. The executable software and data can bestored in various places including for example ROM, volatile RAM,non-volatile memory and/or cache. Portions of this software and/or datacan be stored in any one of these storage devices. In general, a machinereadable medium includes any mechanism that provides (i.e., storesand/or transmits) information in a form accessible by a machine (e.g., acomputer, network device, personal digital assistant, manufacturingtool, any device with a set of one or more processors, etc.).

FIG. 6 provides an exemplary, non-limiting implementation of computercontrol system 425, which includes one or more processors 430 (forexample, a CPU/microprocessor), bus 402, memory 435, which may includerandom access memory (RAM) and/or read only memory (ROM), one or moreinternal storage devices 440 (e.g. a hard disk drive, compact disk driveor internal flash memory), a power supply 445, one more communicationsinterfaces 450, and various input/output devices and/or interfaces 460such as a user interface for a clinician to provide various inputs, runsimulations etc.

Although only one of each component is illustrated in FIG. 6, any numberof each component can be included computer control system 425. Forexample, a computer typically contains a number of different datastorage media. Furthermore, although bus 402 is depicted as a singleconnection between all of the components, it will be appreciated thatthe bus 402 may represent one or more circuits, devices or communicationchannels which link two or more of the components. For example, inpersonal computers, bus 402 often includes or is a motherboard.

In one embodiment, computer control system 425 may be, or include, ageneral purpose computer or any other hardware equivalents configuredfor operation in space. Computer control system 425 may also beimplemented as one or more physical devices that are coupled toprocessor 430 through one of more communications channels or interfaces.For example, computer control system 425 can be implemented usingapplication specific integrated circuits (ASIC). Alternatively, computercontrol system 425 can be implemented as a combination of hardware andsoftware, where the software is loaded into the processor from thememory or over a network connection.

Examples of computer-readable storage media include, but are not limitedto, recordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., compact discs(CDs), digital versatile disks (DVDs), etc.), among others. Theinstructions can be embodied in digital and analog communication linksfor electrical, optical, acoustical or other forms of propagatedsignals, such as carrier waves, infrared signals, digital signals, andthe like. The storage medium may be the internet cloud, or a computerreadable storage medium such as a disc.

Examples of computer-readable storage media include, but are not limitedto, recordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., compact discs(CDs), digital versatile disks (DVDs), etc.), among others. Theinstructions can be embodied in digital and analog communication linksfor electrical, optical, acoustical or other forms of propagatedsignals, such as carrier waves, infrared signals, digital signals, andthe like.

FIG. 8 depicts one view made available by the surgical planning system.In this exemplary embodiment, the view includes a 2D slice of a brainvolume 800, selected by the user, and a virtualized port tool 810 in aspecific pose or orientation of the port, where the tip of the port isin contact with a target point 820 within the brain. The target may bethe location of a pathology within the brain. The embodiment displays aset of tracts 830, which are anticipated to intersect the tool for thisorientation. Tracts are displayed visibly if they intersection the toolon the plane of the current cross-section or within a configurabledistance within a range of the cross-section. In the case of port-basedneurosurgery, an example of this configurable distance may be 3 mm. Thetracts are displayed to the user, and may include red-green-bluecolouring (not shown) to indicate the directionality of the tracts inthree orthogonal directions. Tracts may be displayed as outlines (i.e.,without colour or opacity) if they exist at a configurable distance awayfrom the intersection with the port. Again, for the case of port-basedbrain surgery, this distance may be typically 3 to 10 mm. Thisconfigurable distance may be adjusted to account for the confidence thesurgeon may have in positioning his or her surgical tool relative to theintended position when guided by a surgical navigation system.Consequently, this visualization allows the user to perceive DTI tractintersection information in a space around the tool and around thecurrently visible cross-section (or slice) of the brain 800. Whencomparing FIG. 8 to FIG. 9, it is evident that the number of tractsshown to the user is fewer in FIG. 8, compared to the number of tractsvisible at a different approach angle (or pose) of the same port for thesame target point within the brain, in FIG. 9. From this a clinician mayinfer that the approach of the port tool 810 to the target 820 in FIG. 9would intersect more tracts than the approach of the tool 810 to thetarget 820 in FIG. 8.

In an embodiment, a clinician may use a patient-specific imaging volumeto aid him or her in choosing an optimal entry point into such patient'sanatomy, for example, a sulcus in the brain in order to access a tumor.In a further embodiment, a clinician may rotate the port tool 810 abouta target point 820 located within the brain, and employ an embodiment ofthe disclosed system and method to score alternate approaches, usingpre-determined surgical outcome criteria.

In another embodiment, tract information can be used with a mathematicalcost minimization process in view of the surgical outcome criteria asdisclosed herein to automatically suggest the optimal approaches to atarget 620 location within patient anatomy.

FIG. 9 shows an illustration of tracts intersected by the surgical toolfor a different pose relative to the pose used to visualize intersectedtracts in FIG. 8. In this case, the pose of the tool is depicted asout-of plane to the 2D slice of the patient volume. The tracts arerepresented using the same rules as described in FIG. 8.

FIG. 10 shows a 2D cross-sectional visualization of anticipatedcraniotomy extent using a selected trajectory 1000 and surgical tool810. The craniotomy extent is the size of the skull bone that is cut inorder to access the brain. In general, the smaller the size of this cut,the less the depressurization of the brain, which will reduce the traumato the brain. The trajectory 1000 depicts the path along which the toolis inserted. The trajectory may originate at a virtual engagement point1040 near the surface of the brain and terminate at the target 820. Theoutward extending lines 1020 illustrate the space available above thescalp for manipulating the surgical tool 810 during surgery. The radialsurface extending within the brain region 1030 illustrates the range (orif in 3D, the volume) of brain that will be accessible by the surgicaltool for a given size of craniotomy. The surgical tool can be moved inthis space to visualize tract intersections 830 and the volume of brainregion that will be accessible during surgery. In an embodiment,different sizes of the craniotomy may be selected to evaluate an optimalsize of craniotomy, while evaluating the area of the brain that will beaccessible by the port tool 810 for resecting the tissue region ofinterest. This operation may be performed by a human or may be automatedusing a cost minimization algorithm that incorporates the craniotomysize and volume of accessible region within the brain as theconstraints. The minimum volume of accessible region within the brainmay be, in one embodiment, the volume of identified tumor in the brain.

Other methods for visualizing patient imaging volumes and overlaying DTIinformation and displaying virtual surgical tools against 3D renderingsof 3D sulcal surface maps, or other 3D imaged patient anatomy, will nowoccur to a person of skill in the art and are contemplated.

Furthermore, at least some of the methods described herein are capableof being distributed in a computer program product comprising a computerreadable medium that bears computer usable instructions for execution byone or more processors, to perform aspects of the methods described. Themedium may be provided in various forms such as, but not limited to, oneor more diskettes, compact disks, tapes, chips, USB keys, external harddrives, wire-line transmissions, satellite transmissions, internettransmissions or downloads, magnetic and electronic storage media,digital and analog signals, and the like. The computer useableinstructions may also be in various forms, including compiled andnon-compiled code.

While the Applicant's teachings described herein are in conjunction withvarious embodiments for illustrative purposes, it is not intended thatthe applicant's teachings be limited to such embodiments. On thecontrary, the applicant's teachings described and illustrated hereinencompass various alternatives, modifications, and equivalents, withoutdeparting from the embodiments, the general scope of which is defined inthe appended claims.

Except to the extent necessary or inherent in the processes themselves,no particular order to steps or stages of methods or processes describedin this disclosure is intended or implied. In many cases the order ofprocess steps may be varied without changing the purpose, effect, orimport of the methods described.

What is claimed is:
 1. A system for planning a pathway to a targetlocation in tissue within a patient's body, comprising: a) a storagememory device configured to store therein pre-operative imaging data setfrom at least one imaging modality of an anatomical portion of thepatient's body, and a list of surgical outcome criteria associated withthe anatomical portion of the patient's body; and b) a computerprocessor in communication with the storage device programmed to i)produce, from the pre-operative imaging data, an image of a volume ofthe anatomical portion of the patient's body containing potential entrypoints into the tissue and one or more targets to be approached, andstoring the image of the volume, ii) identify one or more targetlocations to be approached during a surgical procedure and storing theone or more target locations, iii) identify one or more entry pointsinto the patient's tissue and computing point-wise surgical trajectorypaths from the one or more entry points to a first target locationconsistent with the surgical outcome criteria, iv) assign a score to thesurgical trajectory paths to quantify how well they satisfy the surgicaloutcome criteria, and v) store one or more of the surgical trajectorypaths that satisfy the surgical outcome criteria.
 2. The systemaccording to claim 1 wherein said computer processor is programmed toidentify the one or more target locations by comparing the image of the3D volume to an image of a 3D volume produced from an anatomical atlasand identifying one or more anomalous structures in the one or moretarget locations.
 3. The system according to claim 1 wherein saidcomputer processor is programmed to reformat the 3D image data set toconfirm the location of the one or more targets in the 3D volume andstoring the reformatted 3D image data.
 4. The system according to claim3 wherein said computer processor is programmed to reformat the 3D imageset by visualizing the target location in at least three orthogonalplanes to confirm the location of the one or more target locations in 3Dspace.
 5. The system according to claim 1 wherein said computerprocessor is programmed for computing one or more point-wise surgicaltrajectory paths from the one or more designated entry points to thefirst target location with each point-wise surgical trajectory pathpassing through one or more associated waypoints between the one or moreentry points and the first target location.
 6. The system according toclaim 1 wherein the at least one imaging modality is selected from thegroup consisting of ultrasound, magnetic resonance imaging, X-raycomputed tomography and positron emission tomography.
 7. The systemaccording to claim 6 wherein the at least one imaging modality is two ormore imaging modalities.
 8. The system according to claim 7 wherein thecomputer processor is programmed to co-register the images obtained bythe two or more imaging modalities.
 9. The system according to claim 1including comparing the scores of the one or more surgical trajectorypaths to a score of a path of a shortest distance between the one ormore entry points and the first target locations.
 10. The systemaccording to claim 1 configured to plan said one or more surgicaltrajectory paths for a surgical tool, and wherein said computerprocessor is programmed with instructions to insert into the image ofthe 3D volume, at each identified entry point, an image of a surgicaltool to be used to approach the target location.
 11. The systemaccording to claim 10 wherein said computer processor is programmed withinstructions such that upon insertion of the surgical tool into aparticular entry point, the image of the 3D volume of the anatomicalpart is responsively translated and/or rotated to allow forvisualization of the surgical tool at that particular entry point. 12.The system according to claim 10 wherein said computer processor isprogrammed with instructions to assign different colors to tissue typesof different structure and function in the image of the 3D volume toallow for visualization and identification of a particular type oftissue being intersected by the surgical tool to infer specificfunctions being impacted due to tissue intersection with the surgicaltool.
 13. The system according to claim 10 wherein said computerprocessor is programmed with instructions to zoom in on any part of theimage for enhanced visualization.
 14. The system according to claim 12wherein said computer processor is programmed with instructions to zoomin on any part of the image for enhanced visualization.
 15. The systemaccording to claim 12 wherein said computer processor is programmed withinstructions to hide selected portions of the image not in closevicinity to the surgical tool and/or selectively display those portionsof the image in close proximity to surgical tool.
 16. The systemaccording to claim 12 wherein said computer processor is programmed withinstructions to calculate and display an amount of tissue distortionupon the tissue being intercepted by the surgical tool during travelalong the one or more point-wise surgical trajectory paths from the oneor more entry points to the first target location, based on the tissuetype and associated mechanical properties of the tissue.
 17. The systemaccording to claim 1 wherein said computer processor includes a userinterface programmed to receive and store in the storage device inputsfrom a clinician, the inputs including any one or combination of a listof one or more entry points into the tissue, one or more targetlocations to be approached, a first target location to be approachedfirst, and a surgical outcome criteria to be satisfied by the one ormore surgical trajectory paths from the one or more entry points to thefirst one of the one or more targets.
 18. The system according to claim17 wherein said user interface is programmed to accept inputs forlocations corresponding to the list of one or more entry points into thetissue, one or more target locations to be approached and a first targetlocation to be approached first, by a cursor being actuated over saidlocations on the image of the 3D volume.
 19. The system according toclaim 1 wherein said computer processor is programmed with instructionsto, once one or more surgical paths have been computed that satisfy thesurgical outcome criteria, visually display a simulation of the surgicaltool approaching the target along the one or more surgical paths andaccessing all portions of the target to be engaged by the surgicalinstrument.
 20. A system for planning a pathway to a target location intissue within a patient's body, comprising: a) a storage device; b) acomputer processor in communication with said storage device andprogrammed for receiving and storing a pre-operative image data set fromat least one imaging modality and producing therefrom an image of a 3Dvolume of a portion of the patient's body containing potential entrypoints into the tissue and one or more targets to be approached, andstoring the image of the 3D volume; c) a user interface in communicationwith the computer processor programmed to receive and store in thestorage device inputs, the inputs including one or more entry pointsinto the tissue, one or more target locations to be approached, a firsttarget location to be approached first, or surgical outcome criteria tobe satisfied by one or more surgical trajectory paths from the one ormore entry points to the first target; d) wherein said computerprocessor programmed for computing, based on the inputs, one or morepoint-wise surgical trajectory paths from the one or more entry pointsto the first target location to define one or more surgical trajectorypaths from the one or more entry points to the first target locationconsistent with the surgical outcome criteria, and storing the one ormore point-wise surgical trajectory paths in the storage device andvisually displaying the one or more point-wise surgical trajectorypaths.
 21. The system according to claim 20 wherein said computerprocessor is programmed for assigning a score to the one or moretrajectory paths to quantify how well the one or more surgicaltrajectory paths satisfy the surgical outcome criteria, and storing thescores in the storage device and visually displaying the scores of theone or more surgical trajectory paths.
 22. The system according to claim21 wherein said computer processor is programmed for reformatting the 3Dimage data set to confirm the location of the one or more targets in the3D volume and storing the reformatted 3D image data.
 23. The systemaccording to claim 22 wherein said computer processor is programmed toreformat the 3D image set by visualizing the target location in at leastthree orthogonal planes to confirm the location of the one or moretarget locations in 3D space.
 24. The system according to claim 21wherein said computer processor is programmed for computing one or morepoint-wise surgical trajectory paths from the one or more designatedentry points to the first target location with each point-wise surgicaltrajectory path passing through one or more associated waypoints betweenthe one or more entry points and the first target location.
 25. Thesystem according to claim 21 wherein the at least one imaging modalityis one selected from a list comprising of ultrasound, magnetic resonanceimaging, X-ray computed tomography and positron emission tomography. 26.The system according to claim 25 wherein the at least one imagingmodality is two or more imaging modalities, and wherein the computerprocessor is programmed to co-register the images obtained by the two ormore imaging modalities.
 27. The system according to claim 21 whereinthe one or more point-wise surgical paths is two or more surgical pathsand including comparing the scores of the two or more surgical paths,and wherein the inputs include designated point-wise surgical trajectorypath selected on the basis of having a best score relative to the othertrajectory paths.
 28. The system according to claim 21 wherein saidcomputer processor is programmed to confirm the one or more targetlocations as locations to be approached for surgery by comparing theimage of the 3D volume to an image of a 3D volume produced from ananatomical atlas and identifying one or more anomalous structures in theone or more target locations.
 29. The system according to claim 21wherein said computer processor is programmed to compare the scores ofthe one or more surgical trajectory paths to a score of a path of ashortest distance between the one or more entry points and the firsttarget locations.
 30. The system according to claim 20 configured toplan said one or more surgical trajectory paths for a surgical tool, andwherein said computer processor is programmed with instructions toinsert into the image of the 3D volume, at each identified entry point,an image of the surgical tool to be used to approach the targetlocation.
 31. The system according to claim 30 wherein said computerprocessor is programmed with instructions such that upon insertion ofthe surgical tool into a particular entry point, the image of the 3Dvolume of the anatomical part is responsively translated, rotated orboth, to allow for visualization of the surgical tool at that particularentry point.
 32. The system according to claim 30 wherein said computerprocessor is programmed with instructions to assign different colors totissue types of different structure and function in the image of the 3Dvolume to allow for visualization and identification of a particulartype of tissue being intersected by the surgical tool to infer specificfunctions being impacted due to tissue intersection with the surgicaltool.
 33. The system according to claim 30 wherein said computerprocessor is programmed with instructions to zoom in on any part of theimage for enhanced visualization.
 34. The system according to claim 32wherein said computer processor is programmed with instructions to zoomin on any part of the image for enhanced visualization.
 35. The systemaccording to claim 32 wherein said computer processor is programmed withinstructions to selectively hide selected portions of the image not inclose vicinity to the surgical tool and/or selectively display thoseportions of the image in close proximity to the surgical tool.
 36. Thesystem according to claim 32 wherein said computer processor isprogrammed with instructions to calculate and display an amount oftissue distortion upon the tissue being intercepted by the surgical toolduring travel along the one or more point-wise surgical trajectory pathsfrom the one or more entry points to the first target location, based onthe tissue type and its associated mechanical properties.
 37. The systemaccording to claim 21 wherein the portion of the body to be operated onis the patient's brain, and wherein the image of the 3D volume is a 3Dimage of a volume of the brain containing the one or more targetlocations to be approached, and wherein the at least one imagingmodality is magnetic resonance imaging configured for diffusion tensorimaging to give tractography information, and wherein at least onesurgical outcome criteria is to compute one or more preferable surgicalpathways which do not intercept white matter brain tracks, or, one ormore preferable surgical pathways which intercept as few white matterbrain tracks if not all brain tracks can be avoided, or, one or morepreferable surgical pathways which intercept selected white matter braintracks.
 38. The system according to claim 37 wherein said computerprocessor is programmed with instructions to insert into the 3D image ofthe brain of the patient an image of a surgical tool to be used toapproach the target.
 39. The system according to claim 38 wherein saidentry points are entry points into the sulci, and wherein said computerprocessor is programmed with instructions such that upon insertion ofthe surgical tool into a particular entry point, the image of the 3Dvolume of the brain is responsively translated, rotated or both, toallow for visualization of the surgical tool at that particular entrypoint.
 40. The system according to claim 39 wherein said computerprocessor is programmed with instructions to assign different colors tobrain tissue types of different structure and function in the image ofthe 3D volume to allow for visualization and identification of aparticular type of tissue being intersected by the surgical tool toinfer specific functions being impacted due to tissue intersection withthe surgical tool.
 41. The system according to claim 40 wherein one ofthe brain tissue types is brain tracks, and wherein said computerprocessor is programmed with instructions to assign different colors tofunctionally different brain tracks based on a direction they extend inthe brain and/or a function they perform.
 42. The system according toclaim 40 wherein said computer processor is programmed with instructionsto selectively hide selected portions of the image not in close vicinityto the surgical tool and/or selectively display those portions of theimage in close proximity to surgical tool.
 43. The system according toclaim 42 wherein said computer processor is programmed with instructionsto selectively display white matter brain tracts in an immediatevicinity of each surgical path being computed and hide white matterbrain tracks in all other regions of the 3D image of the brain.
 44. Thesystem according to claim 42 wherein said computer processor isprogrammed with instructions to selectively display white matter braintracks being intercepted by the surgical tool along each surgical pathbeing computed.
 45. The system according to claim 43 wherein one of saidinputs relating to said surgical outcome criteria to be satisfied by oneor more surgical trajectory paths from the one or more entry points tothe first target includes selecting which white matter brain tracks tointercept and which to avoid intercepting.
 46. The system according toclaim 37 wherein said computer processor is programmed with instructionsto calculate and display an amount of tissue distortion upon the tissuebeing intercepted by the surgical tool during travel along the one ormore point-wise surgical trajectory paths from the one or more entrypoints to the first target location, based on the tissue type and itsassociated mechanical properties.
 47. The system according to claim 37wherein the targets being approached are tumors, and wherein saidcomputer processor is programmed with instructions to calculate anddisplay an amount of distortion and/or rolling of the tumor whenapproached by, and intercepted by the surgical tool, and to calculateand display the distortion and/or rolling of the tumor for a pluralityof trajectories of approach.
 48. The system according to claim 46wherein said computer processor is programmed with instructions to, onceone or more surgical paths have been computed that satisfy the surgicaloutcome criteria, visually display a simulation of the surgical toolapproaching the tumor along the one or more surgical paths and accessingall portions of the tumor for accomplishing tumor resection.
 49. Thesystem according to claim 48 wherein said computer processor isprogrammed with instructions to display an 3D image of the patient'sbrain including a skull layer.
 50. The system according to claim 49wherein said computer processor is programmed with instructions tomanipulate the surgical tool during said simulation such that a distalend of the surgical tool can access and resect all parts of the tumorwhile at the same time a proximal end of the surgical tool maps out apath it must follow in order to determine a minimum amount of skullmaterial to be removed during a craniotomy procedure prior to thesurgical tool initiating travel to the tumor.
 51. A computer implementedmethod for planning a pathway to a target location in tissue within apatient's body, the computer implemented method executed within acomputer system comprising a computer processor connected to a storagemedium, and a user interface having a user terminal, the methodcomprising the steps of: producing, at the user terminal, at least onepre-operative image data set image of a 3D volume of a patient's bodycontaining potential entry points into the tissue and one or moretargets to be treated; receiving through the user interface of the userterminal, inputs to be stored in the storage medium including a list ofone or more entry points into the tissue, one or more target locationsto be treated, a first target location to be approached first, and asurgical outcome criteria to be satisfied by one or more surgicaltrajectory paths from the one or more entry points to the first one ofthe one or more targets; computing, at the user terminal, one or morepoint-wise surgical trajectory paths from the one or more designatedentry points to the first target location consistent with the surgicaloutcome criteria; storing the one or more point-wise surgical trajectorypaths in the storage medium; assigning a score to the one or moretrajectory paths, at the user terminal, to quantify how well the one ormore trajectory paths satisfy the surgical outcome criteria; storing theassigned scores in the storage medium; and visually displaying selectedsurgical trajectory paths at the user interface.
 52. The computerimplemented method according to claim 51 further comprising the step ofreformatting the image data set, at the user terminal, to confirm thelocation of the one or more targets in the 3D volume; and storing thereformatted 3D image data in the storage medium.
 53. The computerimplemented method according to claim 52 further comprising the step ofreformatting the image set by visualizing one or more the targetlocations in at least three orthogonal planes to confirm the location ofthe one or more target locations in 3D space, at the user interface ofthe user terminal.
 54. The computer implemented method according toclaim 51 further comprising the step of computing, at the user terminal,one or more surgical trajectory paths from the one or more entry pointsto the first target location such that each point-wise surgicaltrajectory path passes through one or more associated waypoints betweenthe entry point and the first target location.
 55. The computerimplemented method according to claim 51 further comprising the step ofcomparing, at the user terminal, the image of the 3D volume to an imageof a 3D volume produced from an anatomical atlas and identifies one ormore anomalous structures in the one or more target locations.
 56. Thecomputer implemented method according to claim 51 wherein thepre-operative image data is a magnetic resonance image of the patient'sbrain, and wherein the image of the 3D volume is a diffusion tensorimage of a volume of the brain containing the one or more targetlocations to be approached, and further comprising the step of computingone or more preferable surgical pathways which do not intercept whitematter brain tracks, or, one or more preferable surgical pathways whichintercept as few white matter brain tracks if not all brain tracks canbe avoided, or, one or more preferable surgical pathways which interceptselected white matter brain tracks chosen by the clinician.
 57. Thecomputer implemented method according to claim 56 further comprising thestep of inserting into the 3D image of the brain of the patient, animage of a surgical tool to be used to approach the target.
 58. Thecomputer implemented method according to claim 57 wherein said entrypoints are entry points into the sulci.
 59. The computer implementedmethod according to claim 58 wherein upon insertion of the surgical toolinto a particular entry point, the image of the 3D volume of the brainis responsively translated, rotated or both, to allow for visualizationof the surgical tool at that particular entry point.
 60. The computerimplemented method according to claim 59 further comprising the step ofassigning different colors, at the user terminal, to brain tissue typesof different structure and function in the image of the 3D volume toallow for visualization and identification of a particular type oftissue being intersected by the surgical tool to infer specificfunctions being impacted due to tissue intersection with the surgicaltool.
 61. The system according to claim 60 wherein one of the braintissue types is brain tracks, and the assignment of different colors tofunctionally different brain tracks are based on a direction they extendin the brain and/or a function they perform.
 62. The computerimplemented method according to claim 60 further comprising the step ofselectively hiding, at the user terminal, portions of the image not inclose vicinity to the surgical tool and/or selectively displayingportions of the image in close proximity to the surgical tool.
 63. Thecomputer implemented method according to claim 62 further comprising thestep of selectively displaying, at the user terminal, white matter braintracts in an immediate vicinity of each surgical path being computed andhiding white matter brain tracks in all other regions of the 3D image ofthe brain.
 64. The computer implemented method according to claim 63further comprising the step of selectively displaying, at the userterminal, white matter brain tracks being intercepted by the surgicaltool along each surgical path being computed.
 65. The computerimplemented method according to claim 64 wherein one of said inputsrelating to said surgical outcome criteria to be satisfied by one ormore surgical trajectory paths from the one or more entry points to thefirst target includes selecting which white matter brain tracks tointercept and which to avoid intercepting.
 66. The computer implementedmethod according to claim 57 further comprising the steps of calculatingat the user terminal, and displaying at the user interface, an amount oftissue distortion upon the tissue being intercepted by the surgical toolduring travel along the one or more point-wise surgical trajectory pathsfrom the one or more entry points to the first target location, based onthe tissue type and associated mechanical properties of the tissue. 67.The computer implemented method according to claim 57 further comprisingthe steps of calculating, at the user terminal, and displaying, at theuser interface, an amount of distortion and/or rolling of the tumor whenapproached by, and intercepted by the surgical tool for a plurality oftrajectories of approach to the tumor.
 68. The computer implementedmethod according to claim 67 further comprising the steps of visuallydisplaying a simulation of the surgical tool approaching the tumor alongthe one or more surgical paths and accessing all portions of the tumorfor accomplishing tumor resection, at a user interface, once one or moresurgical paths have been computed that satisfy the surgical outcomepath.
 69. The computer implemented method according to claim 68 furthercomprising the step of displaying, at the user interface, a 3D image ofthe patient's brain including a skull layer.
 70. The computerimplemented method according to claim 69 further comprising the step ofmanipulating the surgical tool during said simulation such that a distalend of the surgical tool can access and resect all parts of the tumorwhile at the same time a proximal end of the surgical tool maps out apath on the skull that it must follow in order to determine a minimumamount of skull material to be removed during a craniotomy procedureprior to the surgical tool initiating travel to the tumor.
 71. Acomputer readable storage medium having stored therein a computerprogram for planning a pathway to a target location in tissue within apatient's body, the computer program being programmed with steps, which,when executed on a computer, comprises: producing at least onepre-operative image data set image of a 3D volume of a patient's bodycontaining potential entry points into the tissue and one or moretargets to be treated; receiving inputs including a list of one or moreentry points into the tissue, one or more target locations to betreated, a first target location to be approached first, and a surgicaloutcome criteria to be satisfied by one or more surgical trajectorypaths from the one or more entry points to the first one of the one ormore targets; computing one or more point-wise surgical trajectory pathsfrom the one or more designated entry points to the first targetlocation consistent with the surgical outcome criteria; storing the oneor more point-wise surgical trajectory paths; assigning a score to theone or more trajectory paths to quantify how well the one or moretrajectory paths satisfy the surgical outcome criteria; storing theassigned scores; and visually displaying selected surgical trajectorypaths.
 72. The computer readable storage medium according to claim 71wherein the computer program is further programmed for reformatting the3D image data set, at the user terminal, to confirm the location of theone or more targets in the 3D volume; and storing the reformatted 3Dimage data.
 73. The computer readable storage medium according to claim72 wherein the computer program is further programmed for reformattingthe 3D image set by visualizing one or more the target locations in atleast three orthogonal planes to confirm the location of the one or moretarget locations in 3D space.
 74. The computer readable storage mediumaccording to claim 71 wherein the computer program is further programmedfor computing one or more surgical trajectory paths from the one or moreentry points to the first target location such that each point-wisesurgical trajectory path passes through one or more associated waypointsbetween the entry point and the first target location.
 75. The computerreadable storage medium according to claim 71 wherein the computerprogram is further programmed for comparing the image of the 3D volumeto an image of a 3D volume produced from an anatomical atlas andidentifies one or more anomalous structures in the one or more targetlocations.
 76. The computer readable storage medium according to claim71 wherein the pre-operative 3D image data is a magnetic resonance imageof the patient's brain, and wherein the image of the 3D volume is adiffusion tensor image of a volume of the brain containing the one ormore target locations to be approached, wherein said entry points areentry points into the sulci, and wherein the computer program is furtherprogrammed for computing one or more preferable surgical pathways whichdo not intercept white matter brain tracks, or, one or more preferablesurgical pathways which intercept as few white matter brain tracks ifnot all brain tracks can be avoided, or, one or more preferable surgicalpathways which intercept selected white matter brain tracks chosen by aclinician.
 77. The computer readable storage medium according to claim76 wherein the computer program is further programmed for inserting intothe 3D image of the brain of the patient, an image of a surgical tool tobe used to approach the target.
 78. The computer readable storage mediumaccording to claim 77 wherein the computer program is further programmedfor, upon insertion of the surgical tool into a particular entry point,responsively translating, rotating or both, the image of the 3D volumeof the brain to allow for visualization of the surgical tool at thatparticular entry point.
 79. The computer readable storage medium methodaccording to claim 78 wherein the computer program is further programmedfor assigning different colors, at the user terminal, to brain tissuetypes of different structure and function in the image of the 3D volumeto allow for visualization and identification of a particular type oftissue being intersected by the surgical tool to infer specificfunctions being impacted due to tissue intersection with the surgicaltool.
 80. The computer readable storage medium method according to claim79 wherein the computer program is further programmed for assigningdifferent colors to functionally different brain tracks based on adirection they extend in the brain and/or a function they perform. 81.The computer readable storage medium method according to claim 79wherein the computer program is further programmed for selectivelyhiding, at the user terminal, portions of the image not in closevicinity to the surgical tool and/or selectively displaying portions ofthe image in close proximity to the surgical tool.
 82. The computerreadable storage medium method according to claim 81 wherein thecomputer program is further programmed for selectively displaying, atthe user terminal, white matter brain tracts in an immediate vicinity ofeach surgical path being computed and hiding white matter brain tracksin all other regions of the 3D image of the brain.
 83. The computerreadable storage medium method according to claim 82 wherein thecomputer program is further programmed for selectively displaying whitematter brain tracks being intercepted by the surgical tool along eachsurgical path being computed.
 84. The computer readable storage mediummethod according to claim 83 wherein the computer program is furtherprogrammed for selecting which white matter brain tracks to interceptand which to avoid intercepting depending of the surgical outcomecriteria to be satisfied by one or more surgical trajectory paths fromthe one or more entry points to the first target.
 85. The computerreadable storage medium method according to claim 76 wherein thecomputer program is further programmed for calculating and displaying atthe user interface, an amount of tissue distortion upon the tissue beingintercepted by the surgical tool during travel along the one or morepoint-wise surgical trajectory paths from the one or more entry pointsto the first target location, based on the tissue type and associatedmechanical properties of the tissue.
 86. The computer readable storagemedium method according to claim 76 wherein the computer program isfurther programmed for calculating and displaying an amount ofdistortion and/or rolling of the tumor when approached by, andintercepted by the surgical tool for a plurality of trajectories ofapproach to the tumor.
 87. The computer readable storage medium methodaccording to claim 86 wherein the computer program is further programmedfor visually displaying a simulation of the surgical tool approachingthe tumor along the one or more surgical paths and accessing allportions of the tumor for accomplishing tumor resection once one or moresurgical paths have been computed that satisfy the surgical outcomepath.
 88. The computer readable storage medium method according to claim87 wherein the computer program is further programmed for displaying a3D image of the patient's brain including a skull layer.
 89. Thecomputer readable storage medium method according to claim 88 whereinthe computer program is further programmed for manipulating the surgicaltool during said simulation such that a distal end of the surgical toolcan access and resect all parts of the tumor while at the same time aproximal end of the surgical tool maps out a path on the skull that itmust follow in order to determine a minimum amount of skull material tobe removed during a craniotomy procedure prior to the surgical toolinitiating travel to the tumor.
 90. The computer readable storage mediumaccording to claim 71 being the internet cloud.
 91. The computerreadable storage medium according to claim 71 being a computer readablestorage medium.
 92. A system for planning brain surgery, comprising: a)a storage device configured to receive and store therein at least onepre-operative image data set of a patient's brain acquired using atleast one imaging modality; b) a computer processor and associated userinterface in communication with said storage device, said processorconfigured to: produce an image of a 3D volume of the portion of thebrain containing one or more potential sulci entry points into thetissue and one or more targets in the brain to be approached from the atleast one pre-operative 3D image data set and store the image of the 3Dvolume in the storage device; receive, through the user interface, theinputs including at least a list of one or more sulci entry points intothe brain, one or more target locations to be approached, a first targetlocation to be approached first, and a surgical outcome criteria to besatisfied by one or more surgical trajectory paths from the one or moreentry points to the first one of the one or more targets; upon receivingthe list of one or more sulci entry points, the first target to beapproached and the surgical outcome criteria, compute one or morepoint-wise surgical trajectory paths from the one or more sulci entrypoints to the first target location with each point-wise surgicaltrajectory path passing through one or more associated waypoints betweenthe one or more sulci entry points and the first target location todefine one or more surgical trajectory paths from the one or more entrypoints to the first target location consistent with the surgical outcomecriteria; store the one or more point-wise surgical trajectory paths inthe storage medium and visually displaying the one or more point-wisesurgical trajectory paths; and assign a score to the one or moretrajectory paths to quantify how well the one or more surgicaltrajectory paths satisfy the surgical outcome criteria.
 93. The systemaccording to claim 92 wherein said computer processor is programmed tocompare the scores of the one or more surgical trajectory paths to ascore of a path of a shortest distance between the one or more sulcientry points and the first target location.
 94. The system according toclaim 92 wherein said computer processor is programmed for reformattingthe 3D image data set to confirm the location of the one or more targetsin the 3D volume and storing the reformatted 3D image data.
 95. Thesystem according to claim 94 wherein said computer processor isprogrammed to reformat the 3D image set by visualizing the targetlocation in at least three orthogonal planes to confirm the location ofthe one or more target locations in 3D space.
 96. The system accordingto claim 93 wherein the at least one imaging modality is one selectedfrom a list comprising of ultrasound, magnetic resonance imaging, X-raycomputed tomography and positron emission tomography.
 97. The systemaccording to claim 96 wherein the at least one imaging modality is twoor more imaging modalities, and wherein the computer processor isprogrammed to co-register the images obtained by the two or more imagingmodalities.
 98. The system according to claim 93 wherein the at leastone imaging modality is magnetic resonance imaging configured fordiffusion tensor imaging to give tractography information, and whereinat least one surgical outcome criteria is to compute one or moresurgical preferable pathways which do not intercept white matter braintracks, or, one or more surgical preferable pathways which intercept asfew white matter brain tracks if not all brain tracks can be avoided,or, one or more surgical preferable pathways which intercept selectedwhite matter brain tracks chosen by the clinician.
 99. The systemaccording to claim 98 wherein said computer processor is programmed withinstructions to insert into the 3D image of the brain of the patient animage of a surgical tool to be used to approach the target.
 100. Thesystem according to claim 99 wherein said entry points are entry pointsinto the sulci, and wherein said computer processor is programmed withinstructions such that upon insertion of the surgical tool into aparticular entry point, the image of the 3D volume of the brain isresponsively translated, rotated or both, to allow for visualization ofthe surgical tool at that particular entry point.
 101. The systemaccording to claim 100 wherein said computer processor is programmedwith instructions to assign different colors to brain tissue types ofdifferent structure and function in the image of the 3D volume to allowfor visualization and identification of a particular type of tissuebeing intersected by the surgical tool to infer specific functions beingimpacted due to tissue intersection with the surgical tool.
 102. Thesystem according to claim 101 wherein one of the brain tissue types isbrain tracks, and wherein said computer processor is programmed withinstructions to assign different colors to functionally different braintracks based on a direction they extend in the brain and/or a functionthey perform.
 103. The system according to claim 101 wherein saidcomputer processor is programmed with instructions to selectively hideselected portions of the image not in close vicinity to the surgicaltool and/or selectively display those portions of the image in closeproximity to surgical tool.
 104. The system according to claim 103wherein said computer processor is programmed with instructions toselectively display white matter brain tracts in an immediate vicinityof each surgical path being computed and hide white matter brain tracksin all other regions of the 3D image of the brain.
 105. The systemaccording to claim 103 wherein said computer processor is programmedwith instructions to selectively display white matter brain tracks beingintercepted by the surgical tool along each surgical path beingcomputed.
 106. The system according to claim 105 wherein one of saidinputs relating to said surgical outcome criteria to be satisfied by oneor more surgical trajectory paths from the one or more entry points tothe first target includes selecting which white matter brain tracks tointercept and which to avoid intercepting.
 107. The system according toclaim 98 wherein said computer processor is programmed with instructionsto calculate and display an amount of tissue distortion upon the tissuebeing intercepted by the surgical tool during travel along the one ormore point-wise surgical trajectory paths from the one or more entrypoints to the first target location, based on the tissue type and itsassociated mechanical properties.
 108. The system according to claim 98wherein the targets being approached are tumors, and wherein saidcomputer processor is programmed with instructions to calculate anddisplay an amount of distortion and/or rolling of the tumor whenapproached by, and intercepted by the surgical tool, and to calculateand display the distortion and/or rolling of the tumor for a pluralityof trajectories of approach.
 109. The system according to claim 108wherein said computer processor is programmed with instructions to, onceone or more surgical paths have been computed that satisfy the surgicaloutcome criteria, visually display a simulation of the surgical toolapproaching the tumor along the one or more surgical paths and accessingall portions of the tumor for accomplishing tumor resection.
 110. Thesystem according to claim 109 wherein said computer processor isprogrammed with instructions to display an 3D image of the patient'sbrain including a skull layer.
 111. The system according to claim 110wherein said computer processor is programmed with instructions tomanipulate the surgical tool during said simulation such that a distalend of the surgical tool can access and resect all parts of the tumorwhile at the same time a proximal end of the surgical tool maps out apath it must follow in order to determine a minimum amount of skullmaterial to be removed during a craniotomy procedure prior to thesurgical tool initiating travel to the tumor.
 112. A method executed ona computer for planning a pathway to a target location in tissue withina patient's body to be approached, comprising: a) viewing, using acomputer, an image of a 3D volume of the portion of the body containingpotential entry points into the tissue and one or more targets to beapproached produced from a pre-operative image data set, b) instructingthe computer, using a user interface, to store the image of the 3Dvolume in a storage medium; c) inputting to the computer, using the userinterface, instructions to reformat the 3D image data set to confirm thelocation of the one or more targets in the 3D volume; d) inputting tothe computer, using the user interface, designated locations of one ormore entry points into the tissue for a surgical apparatus anddesignating, from the one or more target locations, a first targetlocation to be approached; e) inputting to the computer, using the userinterface, a surgical outcome criteria to be satisfied by one or moresurgical trajectory paths from the one or more potential entry points tothe first target location, and based on the surgical intent, selectingone or more waypoints between the more or more entry points and thefirst target location which are consistent with the surgical outcomecriteria and computing one or more point-wise surgical trajectory pathsfrom the one or more entry points to the first target location with theone or more point-wise surgical trajectory paths passing through one ormore waypoints between the one or more entry points and the first targetlocation consistent with the surgical outcome criteria to define one ormore point-wise surgical trajectory paths from the one or moredesignated entry points to the first target location; f) viewing, usingthe computer, scores assigned to the one or more point-wise trajectorypaths computed by the computer to quantify how well the one or morepoint-wise trajectory paths satisfy the clinician designated surgicaloutcome criteria; and g) instructing the computer, using the userinterface, to store the assigned scores in the storage medium andvisually display the one or more point-wise surgical trajectory paths.113. The method according to claim 112 wherein the portion of the bodyto be operated on is the patient's brain, and wherein the image of the3D volume is an image of a volume of the brain containing the one ormore target locations to be approached, and wherein the at least oneimaging modality is magnetic resonance imaging configured for diffusiontensor imaging to give tractography information, and includinginstructing the computer, using the user interface, to compute one ormore surgical pathways that satisfy at least one of the followingsurgical outcome criteria do not intercept white matter brain tracks,or, intercept as few white matter brain tracks if not all brain trackscan be avoided, or, intercept selected white matter brain tracks. 114.The method according to claim 113 including instructing the computer,using the user interface, to insert into the 3D image of the brain ofthe patient an image of a surgical tool to be used to approach thetarget.
 115. The method according to claim 114 wherein said entry pointsare entry points into the sulci, and including instructing the computer,using the user interface, to, upon insertion of the surgical tool into aparticular entry point, responsively translate, rotate or both, image ofthe 3D volume of the brain to allow for visualization of the surgicaltool at that particular entry point.
 116. The method according to claim115 including instructing the computer, using the user interface, toassign different colors to brain tissue types of different structure andfunction in the image of the 3D volume to allow for visualization andidentification of a particular type of tissue being intersected by thesurgical tool to infer specific functions being impacted due to tissueintersection with the surgical tool.
 117. The method according to claim116 wherein one of the brain tissue types is brain tracks, includinginstructing the computer, using the user interface, to assign differentcolors to functionally different brain tracks based on a direction theyextend in the brain and/or a function they perform.
 118. The methodaccording to claim 115 including instructing the computer, using theuser interface, to selectively hide selected portions of the image notin close vicinity to the surgical tool and/or selectively display thoseportions of the image in close proximity to surgical tool.
 119. Themethod according to claim 118 including instructing the computer, usingthe user interface, to selectively display white matter brain tracts inan immediate vicinity of each surgical path being computed and hidewhite matter brain tracks in all other regions of the 3D image of thebrain.
 120. The method according to claim 118 including instructing thecomputer, using the user interface to selectively display white matterbrain tracks being intercepted by the surgical tool along each surgicalpath being computed.
 121. The method according to claim 120 wherein oneof said inputs relating to said surgical outcome criteria to besatisfied by one or more surgical trajectory paths from the one or moreentry points to the first target includes selecting which white matterbrain tracks to intercept and which to avoid intercepting.
 122. Themethod according to claim 113 including instructing the computer, usingthe user interface, to calculate and display an amount of tissuedistortion upon the tissue being intercepted by the surgical tool duringtravel along the one or more point-wise surgical trajectory paths fromthe one or more entry points to the first target location, based on thetissue type and its associated mechanical properties.
 123. The methodaccording to claim 113 wherein the targets being approached are tumors,including instructing the computer, using the user interface tocalculate and display an amount of distortion and/or rolling of thetumor when approached by, and intercepted by the surgical tool, and tocalculate and display the distortion and/or rolling of the tumor for aplurality of trajectories of approach.
 124. The method according toclaim 123 including instructing the computer, using the user interfaceto, once one or more surgical paths have been computed that satisfy thesurgical outcome criteria, visually display a simulation of the surgicaltool approaching the tumor along the one or more surgical paths andaccessing all portions of the tumor for accomplishing tumor resection.125. The method according to claim 124 including instructing thecomputer, using the user interface, to display a 3D image of thepatient's brain including a skull layer.
 126. The method according toclaim 125 including instructing the computer, using the user interface,to manipulate the surgical tool during said simulation such that adistal end of the surgical tool can access and resect all parts of thetumor while at the same time a proximal end of the surgical tool mapsout a path it must follow in order to determine a minimum amount ofskull material to be removed during a craniotomy procedure prior to thesurgical tool initiating travel to the tumor.
 127. A system for planninga pathway to a target location in tissue to be approached within apatient's body, comprising: a computer having a storage medium, visualdisplay and a user interface, the computer being programmed with a firstcode segment which, when executed on the computer, produces from atleast one pre-operative image data set an image of a 3D volume of apatient's body containing potential entry points into the tissue and oneor more targets to be approached, and displays the image of the 3Dvolume on the visual display; a second code segment which, when executedon the computer, accepts through the user interface and stores in thestorage medium inputs, the inputs including a list of one or more entrypoints into the tissue, one or more target locations to be approached, afirst target location to be approached first, and a surgical outcomecriteria to be satisfied by one or more surgical trajectory paths fromthe one or more entry points to the first one of the one or moretargets; a third code segment which, when executed on the computer,computes one or more point-wise surgical trajectory paths from the oneor more designated entry points to the first target location consistentwith the surgical outcome criteria and stores the one or more point-wisesurgical trajectory paths in the storage medium; and a fourth codesegment which, when executed on the computer, assigns a score to the oneor more trajectory paths to quantify how well the one or more trajectorypaths satisfy the surgical outcome criteria and stores the assignedscores in the storage medium and visually displaying selected surgicaltrajectory paths.
 128. The system according to claim 127 including afifth code segment which, when executed on the computer, reformats the3D image data set to confirm the location of the one or more targets inthe 3D volume and stores the reformatted 3D image data in the storagemedium.
 129. The system according to claim 128 wherein said fifth codesegment reformats the 3D image set by visualizing one or more the targetlocations in at least three orthogonal planes to confirm the location ofthe one or more target locations in 3D space.
 130. The system accordingto claim 127 wherein the third code segment computes the one or moresurgical trajectory paths from the one or more entry points to the firsttarget location such that each point-wise surgical trajectory pathpasses through one or more associated waypoints between the entry pointand the first target location.
 131. The system according to claim 127including a sixth code segment which, when executed on the computer,compares the image of the 3D volume to an image of a 3D volume producedfrom an anatomical atlas and identifies one or more anomalous structuresin the one or more target locations.
 132. The system according to claim127 wherein the pre-operative 3D image data is a magnetic resonanceimage of the patient's brain, and wherein the image of the 3D volume isa diffusion tensor image of a volume of the brain containing the one ormore target locations to be approached, and wherein the third codesegment is programmed to compute one or more preferable surgicalpathways which do not intercept white matter brain tracks, or, one ormore preferable surgical pathways which intercept as few white matterbrain tracks if not all brain tracks can be avoided, or, one or morepreferable surgical pathways which intercept selected white matter braintracks chosen by the clinician.
 133. The system according to claim 132including a seventh code segment which, when executed on the computer,is programmed to insert into the 3D image of the brain of the patient animage of a surgical tool to be used to approach the target.
 134. Thesystem according to claim 133 wherein said entry points are entry pointsinto the sulci, and including an eight code segment which, when executedon the computer, is programmed such that upon insertion of the surgicaltool into a particular entry point, the image of the 3D volume of thebrain is responsively translated, rotated or both, to allow forvisualization of the surgical tool at that particular entry point. 135.The system according to claim 134 including a ninth code segment which,when executed on the computer, is programmed to assign different colorsto brain tissue types of different structure and function in the imageof the 3D volume to allow for visualization and identification of aparticular type of tissue being intersected by the surgical tool toinfer specific functions being impacted due to tissue intersection withthe surgical tool.
 136. The system according to claim 135 wherein one ofthe brain tissue types is brain tracks, and wherein said ninth codesegment is programmed to assign different colors to functionallydifferent brain tracks based on a direction they extend in the brainand/or a function they perform.
 137. The system according to claim 135including a tenth code segment which, when executed on the computer, isprogrammed to selectively hide selected portions of the image not inclose vicinity to the surgical tool and/or selectively display thoseportions of the image in close proximity to surgical tool.
 138. Thesystem according to claim 137 wherein said tenth code segment isprogrammed to selectively display white matter brain tracts in animmediate vicinity of each surgical path being computed and hide whitematter brain tracks in all other regions of the 3D image of the brain.139. The system according to claim 138 said tenth code segment isprogrammed to selectively display white matter brain tracks beingintercepted by the surgical tool along each surgical path beingcomputed.
 140. The system according to claim 139 wherein one of saidinputs relating to said surgical outcome criteria to be satisfied by oneor more surgical trajectory paths from the one or more entry points tothe first target includes selecting which white matter brain tracks tointercept and which to avoid intercepting.
 141. The system according toclaim 132 including a tenth code segment which, when executed on thecomputer, calculates and displays an amount of tissue distortion uponthe tissue being intercepted by the surgical tool during travel alongthe one or more point-wise surgical trajectory paths from the one ormore entry points to the first target location, based on the tissue typeand its associated mechanical properties.
 142. The system according toclaim 132 wherein the targets being approached are tumors, including aneleventh code segment which, when executed on the computer, calculatesand displays an amount of distortion and/or rolling of the tumor whenapproached by, and intercepted by the surgical tool, and to calculateand display the distortion and/or rolling of the tumor for a pluralityof trajectories of approach to the tumor.
 143. The system according toclaim 142 including an twelfth code segment which, when executed on thecomputer, once one or more surgical paths have been computed thatsatisfy the surgical outcome criteria, visually displays a simulation ofthe surgical tool approaching the tumor along the one or more surgicalpaths and accessing all portions of the tumor for accomplishing tumorresection.
 144. The system according to claim 143 including a thirteenthcode segment, which, when executed on the computer, displays a 3D imageof the patient's brain including a skull layer.
 145. The systemaccording to claim 144 including a fourteenth code segment, which, whenexecuted on the computer, manipulates the surgical tool during saidsimulation such that a distal end of the surgical tool can access andresect all parts of the tumor while at the same time a proximal end ofthe surgical tool maps out a path on the skull that it must follow inorder to determine a minimum amount of skull material to be removedduring a craniotomy procedure prior to the surgical tool initiatingtravel to the tumor.